While much has been written of the PLA's modernisation including the latest reforms, focus on the PLAGF has been limited and the material written on them have not delved very deeply into the modernisation's effects on their warfighting techniques. The PLA has seen and is continuing to see immense changes in their organisation, training, and equipment. Overhauling of the command structure and admin functions of the PLA along with introduction of new equipment have made the PLAGF a much more flexible and mobile force, underscoring the PLA's complete transition from defensive attrition warfare to fast-paced manoeuvre warfare.
In the spring of 2014, a task force was formed in Beijing to draw up a reform blueprint for the PLA. It involved over 690 civilian and military departments, 900 serving and retired commanders and experts, 2165 brigade-level and above officers, and ultimately resulted in over 800 meetings and took into account over 3400 comments and recommendations from the rank and file. The blueprint was revised over 150 times and was finalised in November 2015. Subsequently, the PLA underwent thorough reforms, demobilising 300,000 personnel, constituting almost half of non-combat positions and 30% of the officer corps. It is the most comprehensive of all PLA reforms in recent memory and has radically changed the way the PLA operates. A new training syllabus also went into effect in January 2018, having been in the works since April 2013. The overriding priority of the new syllabus is to have a high degree of realism with emphasis on new modes of warfare such as jointness and informationisation.
The PLA reforms are not complete and more will follow. In the last ten years, the salaries and social status of military personnel have been elevated considerably and recruitment is not an issue. Retainment, however, is, and skilled personnel attrition remains a major challenge to the PLA. A rework of the promotion and pay structure is likely planned as are changes to the recruitment schedule and possibly also lengths of service. This should give skilled personnel fairer remuneration, more flexible career paths, and make the military more competitive with the civilian sector. There is also increasing societal pressure on the PLA to relax their selection criteria and start accepting applicants such as college graduates that have passed the cut-off age or aspiring pilots with less than 20/20 vision. As the PLA has expanded their public outreach and interactions especially on social media, it is possible these widespread calls will lead to changes.

Organisation

The PLA's organisation underwent structural, strategic, operational, and tactical changes. The four CMC organs were split up into fifteen smaller departments for better specialisation while accountability was strengthened by making the discipline department and audit office independent. Drastic reform of the CMC organs was something that over 90% of the task force agreed must be done if the reforms were to have any chance of lasting success. This served to destroy existing interest groups, cut bureaucratic bloat, reduce graft, and structurally impede formation of future interest groups and factions. At the same time, military regions were dismantled and their functions transferred to theatre commands and branches, splitting up the operational and administrative responsibilities that had previously been combined. Operations and admin can now be focused upon exclusively by their designated institution without distraction. Towards the smaller scale, group armies and echelons below them were reformed or abolished to maximise combat effectiveness, taking into account improvements in information technology and quality of the recruitment pool.

Strategic

Former Military Regions

The seven military regions were dismantled and their assets along with those of other branches were reorganised under five new theatre commands. The military regions existed as a holdover from the initial thirteen military regions which had been reduced and reorganised into seven over the decades. Their establishment stemmed from administrative and internal state considerations that were relevant decades ago but no longer make much sense today. In addition to their administrative responsibilities, military regions also had operational responsibility for PLAGF units in the region. This intertwining of administrative and operational duties compromised both and military regions were plagued with bureaucratic inefficiencies, graft, poor operational readiness, slow reaction speeds, inconsistent unit qualities, and inadequate jointness. Other branches of the PLA had their own independent chains of command and joint operations were very much a matter of compromise and negotiation between different branches rather than routine and seamless affairs. There have been cases in the past where pre-arranged joint exercises were cancelled or downsized at the last minute because one or more branches did not attend.

Theatre Commands

Theatre commands have operational control of most units within their specified zones, including ground, sea, air, support, and some rocket units, breaking down C3 barriers that previously existed between branches and even between different departments of the same branch. The consolidation of different unit types from different branches under a unified command has led to a huge increase in joint operations and exercises. Indeed, theatre-level joint operations is one of the four main categories of training topics under the new syllabus. Whereas military regions could not order joint exercises into being due to a lack of authority over non-PLAGF units, theatre commands have no such issue. Theatre commands are explicitly not responsible for force planning or administration, freeing them to focus all their effort on preparing and training against their reference threats. Force planning is now conducted by the newly empowered branches. Previously, the CMC organs played a large role in the force planning of the PLA's branches which was detrimental as the CMC had been dominated by PLAGF elements and failed to fully understand or appreciate the specific needs of other branches, nor, due to their need to consider those other branches, did they consider the PLAGF's specific needs either. The result was suboptimal force planning for everyone.
Five theatre commands were established to address specific threats instead of internal priorities a la military regions. Whichever direction has notable threats deserving of dedicated consideration, a theatre command was established to face it. The resulting theatre commands coincide with the four cardinal directions plus a central theatre. The Eastern Theatre Command was established to finish the civil war as well as face the East Asian threat consisting of Japan and USPACOM with possible ROK involvement under certain conditions; the Southern Theatre Command was established to face the South East Asian threat consisting of USPACOM, Vietnam, and a secondary focus on the ROC; the Western Theatre Command was established to face the Central and South Asian threat, consisting of India and USCENTCOM; the Northern Theatre Command was established to face the Korean Peninsula; and the Central Theatre Command was established as a strategic reserve. It's worthwhile to note that theatre command force allocations are not set in stone and units can and are shuffled around the country depending on need. While the general staff of each theatre focuses their preparation and training on the threats in their axis, their job at the fundamental level is to use whatever forces they are given to the best effect. As to what forces they actually get in a war; the CMC will decide that when the time comes.

Joint Logistics Force

To better address wartime requirements, the operations-focused Joint Logistics Force (JLF) was established, unifying logistics throughout the PLA. It consists of a main logistics centre in Hubei and a series of supporting logistics bases in each theatre directing the logistics brigades within. The new brigades are more flexible and deployable, and the JLF as a whole is focused on wartime effectiveness, devoting more preparation and training to carrying out their mission while subject to enemy action. The integration of the JLF in theatre command HQ makes it the sole logistics coordination hub, replacing the previous system where each branch had a separate supply chain coordinated at different locations by different people. Concentrating the C2 of everyone's logistics at a single location overseen by a single team makes joint operations much easier to coordinate and sustain. The advent of logistics brigades further signifies the PLA's new focus on long-distance sustainment of fighting forces as a brigade is a deployable and mobile unit capable of crossing vast distances while a base or centre or depot is inflexible and immobile. Proliferation of brigades thus entails the making mobile of capabilities that had previously been largely static.
The JLF experienced its first real-world challenge during the 2020 Jan-April Hubei lockdown where they were tasked with the operation and manning of converted and field hospitals at the SARS-CoV-2 outbreak epicentre. The JLF was able to assemble over 4000 PLA medical personnel, the majority of whom had prior experience with epidemic response to SARS and/or Ebola. They were transported to their posts in three batches from Jan 24 to Feb 17 from around the country via airlift, high-speed rail, and motorway. In the initial stages of the lockdown, they also provided the first stocks of medical equipment and materials, buying time for the civilian response. However, while the JLF demonstrated its ability to rapidly mobilise men and material nationwide and relocate them in close coordination with civilian counterparts, its wartime capability to sustain expeditionary forces under theatre command direction was not put to the test.

Operational-Tactical

Group Armies

Group armies (GA), the basic operational manoeuvre element of the PLA, have been reduced in number but made larger on average and more consistent. Eighteen GAs existed before the reforms with considerable variation in strength and capabilities between them, e.g. some had no organic aviation and some had just a few brigades while others were loaded with divisions. Five GAs were disbanded as part of the reforms and the remaining thirteen have been standardised with six manoeuvre brigades (except 82nd GA which has seven), an air-defence brigade, an artillery brigade, an aviation brigade, a special warfare brigade, and two or three support brigades, possessing fifty to sixty thousand personnel in total. The common framework across all thirteen GAs allow for flexible attachments and tasking of subordinate units depending on need, facilitating tailored and proportional responses to a variety of contingencies from border skirmishes to artillery exchanges to full blown war. As the largest manoeuvre formation of the PLAGF and in line with the PLA's evolution into a more deployable and expeditionary force, the GA's organic elements such as signals, recce, and EW have been reinforced with assets previously kept at higher echelons giving it enhanced independent operating and sustainment capability. GAs are the prime candidate for deployment abroad if PRC armed assistance is ever requested as they have abundant teeth while possessing enough of a tail to avoid being reliant on local support which cannot be assumed sufficient or even available at all times. I can plausibly see three GAs deployed to the DPRK on short notice without much difficulty with another three held in reserve across the border.

Brigades

The PLAGF has pivoted almost completely to combined-arms brigades and combined-arms battalions. Manoeuvre divisions and regiments have all been abolished except in the Xinjiang Military District where the poor infrastructure and sparseness of the region suits the retainment of divisions, and the Beijing Guard Area which is tasked with protecting the leadership and is not very important. A combined-arms brigade has four combined-arms battalions, a recce battalion, an artillery battalion, an air-defence battalion, a support battalion, and a sustainment battalion. It resembles a smaller version of its superior GAs and a larger version of its subordinate battalions. This modular matryoshka-like structure brings about new capabilities but also new challenges for brigade commanders. While a brigade is normally a tactical level asset, as the nature of warfare has evolved, the operational level of war has been pushed further and further down. In some cases, the brigade echelon is the operational level as the conflict could well be over by the time corps or army echelons respond. The pivot to modular combined-arms brigades is an acknowledge of this trend and the structuring of manoeuvre brigades to resemble a small GA streamlines their employment as operational level assets among other benefits. As a result, brigade leadership now have to be familiar with the employment of his unit both operationally and tactically and everywhere in between.
In addition to manoeuvre brigades, a large part of the PLAGF's combat potential comes from specialised brigades. The most prominent and integral to normal operations is the artillery brigade with one allocated to every GA and two independent. A typical artillery brigade has four or five tube battalions and one or two LR-MLRS battalions. They are responsible for coordination of massed fires against targets both requested by line units and scouted organically as well as those assigned from above. Aviation brigades play an increasingly important role but their current influence is constrained by the limited number of helicopters. Two aviation brigades have been formed into aerial assault brigades and it is believed that all GAs will eventually get the same treatment pending helicopter fleet expansion. Special warfare brigades provide elite infantry capability in situations where mechanised infantry is unsuited. These include prolonged reconnaissance in hostile territory, warfare in terrain inaccessible to vehicles, MOUT, counter-terrorism, and operations requiring special insertion such as swimming, airdrop, powered parachuting, vertical insertion, etc. Air-defence brigades provide mobile hard-kill protection as well as EW capabilities relevant to anti-air. Each missile battalion in the brigade is capable of providing an air-defence umbrella of radius 20-70km depending on the SAM system equipped. There is sometimes also a towed AAA battalion to provide point defence. The remaining support brigades, some of which are organic to GAs while others are theatre command subordinates, provide EW, signals, strategic ISR, engineering, repair, chemical-defence, medical, and logistics support.

Battalions

Line battalions in the PLAGF were transformed from homogeneous battalions into combined-arms battalions. The former were either tank or infantry. They had limited organic sustainment capabilities and were typically issued simple fire and manoeuvre orders. Combined-arms battalions, by comparison, comprise over a dozen specialisations including infantry, tank/assault gun, artillery, anti-tank, anti-air, recce, signals, sapper, field repairs, chemical-defence, and medical among others, and are twice the size of old line battalions. A typical tracked combined-arms battalion has two tank companies, two mechanised infantry companies, a firepower company with indirect fires and AT, and a support and sustainment company. The wheeled and motorised battalions are similarly organised with some differences in vehicle type distribution. They are designed to give commanders the ability to seize initiatives and hold objectives without needing to wait for higher-echelon support and are typically given objectives and missions instead of simple orders. The universal conversion from homogeneous battalions to combined-arms battalions have made battalions the smallest and most manoeuvrable fighting element in the PLA capable of sustained independent operations.
Much has been written on the effects of the transformation to combined-arms battalions on the rank and file, and literature on the topic is abundant. One of the most common remarks regarding the new battalions is the drastic increase in number of technical specialisations. The addition of these specialisations and capabilities to the battalion has necessitated the establishment of a battalion staff to advise and assist the CO who previously only had his deputy and political officer for support. The staff consists of a chief of staff and four functional positions; operations, fires, recce, and combat service support. The latest reforms allow distinguished NCOs to receive training and education previously reserved for officers. These newly-qualified NCOs have begun filling functional positions in battalion staffs, becoming the first staff NCOs in PLA history. The recce specialist is not only a staff member but an active participant in the field and regularly accompanies recce detachments on missions. The fires specialist, in addition to his usual role of organising battalion fires, is often responsible for coordinating with aviation assets since he has the best understanding of where to apply aerial firepower. On top of a staff, the battalion HQ has also been given a chief of NCOs who is in charge of coordinating the battalion's day-to-day life and ensuring the leadership is aware of the situation with the rank and file.
Not only have support assets been made organic to the battalion but control has also been pushed down to line units. For example, to request field repairs, line units previously had to go through the company, battalion, regiment/brigade, and sustainment contingent HQs before reaching the field repair detachment to relay their whereabouts and the nature of the damage. Line units now have direct contact with field repair detachments and can bypass all other echelons, saving vast amounts of time. Similarly, medical teams now accompany line units during an assault enabling them to provide medical care to wounded immediately. However, this necessitates greater tactical proficiency on part of the medical personnel as they no longer reside in the rear only to arrive on scene after the battle is over or has moved on. They are now required to know the kill radii of various munitions, drive AFVs (armoured ambulance), operate information terminals, understand manoeuvre instructions, operate self-defence weaponry, use different types of cover, etc. The experiences of battalion personnel after the reforms reflect the experience of the PLA as a whole; higher competencies are required from everyone.

Equipment

The PLA's new hardware in the air and naval domains have attracted the lion's share of public attention. However, the ground forces have also been actively modernising. The first examples of modern equipment departing from Stalinist-era designs began appearing in the PLA during the 1980s, some having started development in the preceding decade while others were imported from the newly-accessible West. Examples include the first universal chassis SPG, first MBT with a computerised FCS on a non-T-54 chassis, and the TPQ-37 counter-battery radar. However, these pieces of equipment were expensive for the cash-strapped China of the 1980s and procurement numbers were nowhere near enough to equip the entire PLAGF. Only a small number of these systems were procured for high-priority units. Both that generation and the preceding Stalinist generation of equipment are currently being retired.
An intermediate generation of equipment appeared in the 90s and 00s and forms the bulk of the PLAGF inventory. These include the ZTZ96/A, ZTZ99, PLZ05, PLC09, PLL05, HQ7A/B, PGZ04A, ZSL92, PHZ89, AFT09-carrier, and ZTS63A among others. They are typically characterised by tech inferiority in terms of individual subsystems performance but a decent overall performance. Through careful systems engineering involving balancing design requirements, keeping doctrine in mind, and procuring of meaningful numbers, these systems are generally able to fight on comparable terms with contemporaries as part of a combined-arms force. However, there are distinct shortcomings to these systems largely due to limited budget or limited tech base at the time of development. For example, the ZTZ96/A and ZTZ99 do not have an integrated powerpack and engine/transmission changes take many hours; the ZSL92 is not particularly well-protected and its carrying potential is constrained by its small size; the AFT09 requires LOS to engage its targets putting it at high risk of counterfire; and the PLZ05 makes inefficient use of hull volume and thus only carries 30 rounds while the K9 carries 48 rounds and the PzH 2000, 60.
The next generation, which comprises the majority of current procurement, is an evolution of the intermediate generation that addresses many of their shortcomings and are generally competitive with global counterparts. These include the ZTZ99A, ZTZ96B, ZBD04/A/B, ZBL08, CSK141, PHL03/A, PLZ07/A, PLZ05B, PLZ10, ZBD05, PGZ09, HQ16A/B, etc. A large amount of information technologies have been incorporated into this generation and they can be considered the PLA's first foray into networked warfare. Procurement of these systems continue but first few examples of the next generation are beginning to supplant them in production.
The new generation's poster child is ZTQ15 but also includes the AFT10, "625" AAA, PLC161, PLC171, PLC181, PHL191, new 8x8 family, and arguably the PHZ11, PHL11, HQ17/A, and CSK181. This generation is characterised by a very high degree of modularity, informationisation, automation, and limited relation to Cold War designs. Certain Cold War elements persist such as the L7 105mm, 2A18 122mm, 122mm MLRS, and the 9K330 Tor configuration but overall the new generation can be considered distinct from Cold War systems. Future members of this generation will include the next-gen IFV and next-gen tracked SPG. It is unclear whether the next-gen MBT will be part of this generation or the one thereafter, it depends on how radical the technology employed is and how long it takes those technologies to become practical.
In addition to ground systems, the PLAGF is expanding procurement of helicopters. Currently, the PLAGF has a helicopter shortage especially in the multipurpose 10t weight class but with the introduction of the Z-20, this issue will see some mitigation throughout the next two decades. The current helicopter fleet numbers just over 1000 and minimum requirements for the entire PLA is likely at least double if not triple that. The Z-10 provides an initial critical mass of attack helicopters but it has been confirmed by industry and PLA sources that a heavier follow-up is in the works. It is hinted that the new heavy attack helicopter benefits immensely from the Z-20's powertrain and powerplant and may resemble the Huey-to-Cobra transformation. In addition to Z-20, the Z-8G and Z-8L provide supplementary heavy-lift capability transporting ATVs, buggies, tankettes, artillery pieces, etc., and are important components of heliborne assault forces, a unit type that the PLA will likely expand as helicopter numbers continue to rise.
Unmanned systems were adopted beginning in the mid 90s and are increasingly ubiquitous. Lightweight drones like the DJI Mavic, Harwar H16-V12, and CH-902 are hand-launched and man-portable and are thus given to infantry for recce and light air-support. Larger BZK008s and JWP02s fly missions up to 100km away for brigade recce and arty FO while even larger and faster drones like the SX500 provide targeting information up to 300km away for VLR-MLRS like the PHL191. UGVs recently began equipping combat units possibly in a testing and evaluation capacity. The decade leading up to 2020 saw multiple PLA-hosted UGV competitions with both state institutes and civilian companies participating during which multiple models earned the PLA's confidence.
Individual gear is also an area where the PLA has begun modernising albeit not really pushing boundaries. The individual soldier's kit that debuted in the 2019 October Parade began development as part of Project 1224 and is known to consist of new small arms, fatigues, camouflage, body armour, helmet, backpack, and information systems including a tactical display eyepiece and personal IFF system, among others. Relegated to the backburner for decades, individual gear has recently become a priority as funding for the PLA has increased in line with national wealth. However, the PLA remains conservative with design and the kit doesn't appear to feature anything that hasn't already been tried and tested globally. Introduction of the new kit began in late 2019 and the entire process of reequipping two million servicemen is planned to take three years to complete.

Information

A large part of the organisational reforms have been enabled by new information systems including vehicles and terminals supporting the Integrated C4I Complex (ICC) that began development in early 2004 and was first introduced to the PLA across all branches in 2010. The successful development of the ICC was recognised with the State Award for Scientific and Technological Progress Special Class, an award typically given to one to three projects of great significance to the country every year. Other projects that have been given the same award include the DF-31, J-10, and KJ-2000. The ICC unified the hundreds of disparate C4ISTAR systems developed by different branches and departments of the PLA in the twenty years leading up to 2010 and has arguably contributed more to increasing PLA combat effectiveness than any other system in recent memory.
Within most combined-arms brigades, C4ISTAR networks link every vehicle and select infantry such as FO and recce together into a singular battlefield map accessible to all terminals. This allows all vehicles to constantly be aware of friendly positions and identified enemy positions as well as the status of all nodes including their health, munitions count, fuel load, current orders, etc. The commander is able to seamlessly take in the battlefield picture including recommendations from his staff and orders from above, and issue complex orders with a keyboard, a process much more efficient and accurate than traditional voice radio. Some brigades have also compiled databases of the performance parameters of their systems and personnel in a variety of environments and situations. This helps units to construct more realistic training scenarios, make fairer calls during confrontation exercises, and find the most effective methods of doing things supported by empirical data.
If the brigade is subject to electronic attack, standard operating modes should be able to sidestep the disruption by frequency hopping or other signal processing magic. If the attack is especially sophisticated or powerful, friendly EW assets both organic and higher-echelon can respond in the EM spectrum or use support measures to locate the source of the disruption and task fires with its destruction. Failing that, the network has the option to transmit simpler and more powerful packets that are difficult to obfuscate completely, up to and including Morse code. Wired communications can also be used between nearby stationary elements. As a last resort, signal flags are carried aboard every fighting vehicle in the brigade.

Fires

Hailed as the god of war, artillery systems have been given priority development and procurement by the PLA since their founding, the last twenty years being no exception. The PLA operates tube and rocket artillery of various calibres, both guided and unguided. Tube artillery mostly has three echelons; battalion, brigade, and corps. Battalion tubes are self-propelled vehicles armed with the 2A80, a gun-mortar system that can perform well over a wide range of elevation angles. They began entering service en masse in the mid-00s. Effective range with conventional munitions is <15km, about the maximum expected for battalion-organic recce and FO. Brigade tube fires is provided by 2A18s with a max effective range of <25km. They are mounted on a variety of platforms, most of which are self-propelled but some brigades still operate towed systems. Corps tube fires is provided either by 152mm or 155mm L52 guns developed on the basis of Gerald Bull's 155mm L45s. L52s have a range of 38km firing base-bleed rounds with tight dispersion and low cost, traits desirable for the voluminous round consumptions that characterise HIC. Larger calibres including 203mm were tested but abandoned as the PLA struggled to find a use for them with the introduction of large-calibre MLRS.
The bulk of tactical fires is provided by thousands of 120mm gun-mortars organic to battalions and 122mm guns organic to manoeuvre brigades; the calibres chosen for their good balance of firepower, cost, and handleability. 120mm systems include the PLL05 and PLZ10 while 122mm systems include the tracked PLZ07/A/B and PLZ89, 8x8 PLL09, truck-based PLC09, PLC161, PLC171, and the towed PL96. 152mm and 155mm guns provide corps fires although the former are increasingly rare and should be entirely gone within a couple years. The PLA's adoption of the 155mm calibre was motivated primarily by the range offered by the L45 and subsequent L52 tubes which made it possible for former div arty and corps arty to support a large number of subordinate manoeuvring units at once. Although the 155mm is capable of firing ERFB and rocket-assisted rounds with ranges exceeding 50km, the PLA chooses not to as the dispersion of those rounds is poor. Standard or base-bleed rounds comprise the bulk of PLA massed-fires expenditure. Current systems in service include the PLZ05/A, PLC181, and a few PLZ45s in the PLA Armour Academy.
Rocket artillery primarily come in 122mm and 300mm with limited numbers of 107mm and 370mm. 122mm is mostly organic to brigades and have a maximum range of 40km. 300mm belonged to dedicated LR-MLRS brigades until they were disbanded during the reforms and folded into artillery brigades which were given expanded ISTAR capabilities allowing them to service the 150-180km range of the PHL03s. The 370mm PHL191 with an estimated range of more than 300km and its requisite ISTAR assets are entering service beginning with the 72nd GA's artillery brigade. Large-calibre rocket artillery sees the most PLAGF use of precision munitions and live-fire footage of Beidou-guided and bunker-busting rounds from PHL03s are very common.
For the newer systems, the entire gun or tube-laying process is automated and all relevant data is digitally communicated and processed including firing orders, positions, atmospheric data, radar-captured trajectory parameters, and target status after each salvo. The time from FO requesting a fire mission or CBR detecting enemy rounds to guns firing is typically less than a minute for guns already on standby. For truck-based SPGs, the time from first receiving firing orders while on the march to completion of the firing mission and being on the march again is less than five minutes. The time required for SPGs built on AFV chasses that don't require adjustment of the suspension system and lowering/raising of bracing spades is even less. For the entire duration of the mission, the crew only needs to park the vehicle and load the gun as everything else is automated.
Dedicated anti-tank systems have been since the 1950s and continue to be part of artillery units in the PLA. At the battalion echelon, the AFT11 has just entered service so most battalions still use AFT07s and PF98s. At the brigade echelon, AFT10s have proliferated to a very healthy degree with lower-priority units still operating AFT09s. The AFT10 is an optical fibre-guided NLOS optional man-in-the-loop or fire & forget heavy missile with a 10km range suitable for anti-armour, anti-vehicle and anti-fortification duties, and is also capable of engaging slow low-flying targets. The missile is entirely fibre-guided with no radio-guided portion of flight thus rendering it almost impossible to jam, a capability the PLA considers crucial in a war against opponents with advanced EW systems such as the US and to a lesser degree, the ROK. Gun-based anti-tank systems have been entirely withdrawn from service since 2019.

Direct-Fire

The PLAGF direct-fire assault fleet in non-amphibious units totals roughly 4850 vehicles. ZTZ59/79s amount to roughly 500, ZTZ88A/Bs around 350, ZTZ96s around 800, ZTZ96As around 1050, ZTZ99s and 99As both around 500, ZTQ15s around 150, and ZLT11s around 1000. Everything older than ZTZ96A are either obsolete or so worn down from intensive training that they all need to be retired within a decade. The ZTZ59/79s will be the first to go, likely within a couple of years. Their numbers have already fallen drastically in the past three years from ~2500 in early 2017 to roughly 500 today. ZTZ88s will follow shortly as quite a few of them are already serving as placeholders and not tanks. ZTZ96s have been run hard for over twenty years and many vehicles are quite worn, they will likely be replaced by ZTZ96Bs and ZTZ99As. ZTZ96A and ZTZ99 are relatively new, their FCS are fully computerised and compatible with informationisation upgrades; their replacements can wait a while. ZTZ99A and ZTQ15 are currently in production and will remain so for the immediate future.
ZTZ96Bs were previously thought to be unnecessary but the intense wear on ZTZ96s, exacerbated by the latest reforms, means over 1400 tanks need replacing in the immediate future. Furthermore, the restructuring of the Xinjiang divisions strongly suggests there will be an expansion in the tank fleet by 100-400 vehicles, making the actual number of new tanks needed 1500-1800. Having them all be ZTZ99As and ZTQ15s is financially untenable. The ZTZ96Bs will thus play a big role in satisfying this demand. ZTQ15s will populate at least two brigades but more may follow. The Marines also operate the ZTQ15 and will probably expand their fleet as well. ZLT11 and its replacement are being procured to equip the high-mobility 8x8 brigades. Another 350-450 8x8 assault guns are needed to fill the existing ORBAT with more needed for the Marines and possibly also non-manoeuvre units such as border defence and Beijing Guards.

Case Study: ZTQ15

The ZTQ15 is arguably the most recognisable component of the PLAGF's equipment modernisation; a great many people who know practically nothing about the PLA or China as a whole nevertheless know the PLA has a new light tank. The ZTQ15 is thus a good case study to illustrate the direction of the PLA's hardware upgrades. It was tailored for operations in hostile environments such as altitudes over 4500m above sea level and soft muddy terrain. Its V8 engine with a bore diameter of 132mm, stroke length of 145mm, and maximum RPM of 2600, outputs 660kW of maximum continuous power, giving the 33t vehicle a PWR of 20kW/t. To overcome the thin air of the Plateau, the engine is equipped with a two-stage turbocharger that minimises power loss. It is also equipped with a warmer to facilitate quick ignition in extremely cold weather. The engine is coupled to a hydro-mechanical automatic transmission together as a powerpack that can be swapped out within half an hour. The suspension is a semi-active torsion bar system sporting electronically controlled viscous dampers with adjustable orifices that are narrowed or widened in real time depending on sensor readings, providing a smoother ride and reducing crew fatigue, important in the oxygen-sparse atmosphere. If the system breaks down, it simply becomes a passive viscous damper that still provides decent ride quality.
Due to its unique operating environment of highly adverse and isolated terrain where resupply and replacements have great difficulty reaching, the ZTQ15 is designed with multipurpose functionality to get as much bang for the buck as possible. Its FCS is integrated with both direct and indirect fire modes, allowing ZTQ15s to stand in for howitzers if needed. This is achieved by equipping the vehicle with high-precision inertial measurement units and Beidou receivers connected via CAN bus to a central computer. This allows its position and orientation in space to be precisely known so that the battalion or brigade fires director can construct an accurate spatial representation of shooters and targets in 3D and accurately plan indirect fires. Another feature enabled by constant position and orientation awareness is that a ZTQ15 can hand over prosecution of a target to another ZTQ15 in the network if it's unable to prosecute the target itself due to, say, a damaged gun or lack of ammo; essentially remote-controlling someone else's gun to shoot whatever it's looking at even if the target is obscured to the shooter vehicle. This is possible because every vehicle in the network knows its position and orientation relative to everyone else, and if one vehicle knows the position of the target in a 3D space, everyone does.
Many of ZTQ15's features such as FCS automation, digital information displays, high-power-density diesel engine, and networked fleet-based combat lay the foundations for the PLA's next-gen MBT. Current in-service FCS already automate target range-finding, tracking, and leading. This leaves the gunner responsible for target acquisition, firing, and damage assessment. When not engaging a target, the gunner is also responsible for scanning the highest-threat sector where the turret is pointed, usually frontal. Further refinement of automation technologies in the next ten years could mean the gunner only has to spot or confirm an enemy and the FCS will do the rest. The commander's communication and scanning functions have also been automated to a large degree. Recent developments in wearable displays and augmented reality technology promises even greater improvements in this field for both the gunner and commander. Drivers too have an increasingly easy time as old unassisted tillers turned into steering wheels while transmissions became smoother then fully automatic. Vehicle parameters that required driver attention have gradually come under the stewardship of electronic control units, freeing up drivers to pay greater attention to their surroundings.
It is thus being seriously considered to merge the gunner and commander into one position and expand the driver's role to include communications and forward sector scanning for the next-gen MBT. The resulting two-man crew can each have an 80cm-wide workspace and be protected by a healthy amount of side armour without the vehicle exceeding 3.5m overall width or be any heavier than existing MBTs. The unmanned turret can be lightly armoured, cutting turret weight by more than ten tonnes which can then be devoted to more armour for the crew. More refined automation and seamless integration and presentation of imagery and data from onboard and offboard sensors could allow the next-gen MBT to have situational awareness superior to today's tanks in spite of a reduction in crew size. The ZTQ15's extensive use of network systems and new information terminals should give Chinese tank designers hard data and operational experience that will help them identify promising approaches for the next-gen MBT. However, successful development of informationisation and automation to a degree sufficient for a two-man crew in a reasonable timeframe is not guaranteed and it's very possible that the next-gen MBT will retain a three-man crew. Regardless, the ZTQ15 is a good indicator of the direction the PLA is taking with their new equipment.

I'm 27. I used to work back in my home country India 2 years back as a software developer. I left that job because they put too much load on me even before I was familiar with their technology. Fast forward and I'm working in a company in Kuwait where I found myself working as an assistant engineer who deals with automation. Apart from the position, there is nothing much to tag about in the salary. I barely make above the minimum wage people make here for the kind of job I do. There is nothing in this country you engage in. It's depressing. It's always the home-work-home cycle. Back in my home, my parents are alone too as I am a single child. I feel like going back to my own beautiful country and look for an opportunity (which is not so easy due to the high population) there to start working there and for once not feel like a slave working in a foreign land. I would also be able to learn new things and improve myself on my career front. Being in my current job, I don't think I'll be able to gain much knowledge as they are not innovative. But everyone who hears my plan dismisses it as something stupid and asks me to stay here in Kuwait. Their logic is that it's very difficult to find a job in India and even if you find one, it won't pay me as much as I get here. Well, they are not exactly wrong. But finding a job in India is not as easy as in other parts of the world. Especially when you have a surplus of qualified population. Yet, I have hope. My dream has always been to learn so much about technology and create something new and innovative for the world. So far, I have not been able to do that. And I strongly believe that I will be able to achieve that on leaving this job and going home. Working here is like being in a rat race. Trying to earn more. And working without any goals. If we want to achieve something which others haven't, we have to do something different. Otherwise, we'd take the same steps they took. The path to success is always the less taken one. It'll be full of hardships and twists. But only those who thread that path and finish succeed in their life. It's with this thought I have come to my decision. Do you have any experience of doing something similar? Quitting your job to chase your dreams? And what followed?

Job Link to apply: https://www.yobitel.com/careers-fullstack-enterprise-arch
Environment:
Keeping evolution as Constant, Yobitel makes a platform for every individual and organization to reinvent and innovate their business during each convergence phase in technology.
In Yobitel we’re committed to ensuring everyone in the company continues to benefit from working in an environment of cross-departmental collaboration, where all contributions are recognized and acknowledged.
We’re a passionate, hardworking, and sociable team and we’re looking for like-minded individuals to join us as we continue to grow.
There’s never been a more exciting time to join the company. And the successful applicant to this role will benefit from running projects absolutely integral to our growth plans over the coming years.
​
Job Theme:
We are looking for an experienced Enterprise Architect Full-Stack to come on board on a permanent basis and work in our Bangalore office who have qualified experience to work in a fast pace AI-driven platform.
​
Responsibilities:
​

• Experience in Agile, full-stack development experience covering primarily either ReactJS/MEAN, AngularJS frameworks for UI and/or NodeJS framework for microservices development.
• Good knowledge in RESTful API design and implementation, Spring Boot framework.
• Role Specific Capabilities & Knowledge NodeJS backend skills- Mandatory Experienced in developing web-based applications using ReactJS or AngularJS frameworks.
• Experience in DevOps process and development automation - unit and functional test automation and continuous integral delivery.
• Actively contributing to our work on fascinating Machine Learning problems, applying your experience in Python, C++, or Golang and your skills in cloud-based services (AWS, GCP, Azure), Javascript.
• Responsible as Senior and a Back End Engineer who will be working in the technology team and working with the R&D team to implement new components.
• Creating new features for web and mobile application platforms.
• Producing high-quality code and Support and maintain existing applications and customers.

Required Skills:
​

• Solid experience in Python or Node.js, software engineering. Enterprise Architecture, TOGAF, and SOA are highly desirable.
• Well versed in HTML5, CSS3, Bootstrap, and other front-end technologies, good in RDBMS such as Oracle and Mysql.
• Exposure to NoSQL databases such as MongoDB and Cassandra is an advantage.
• Experience in building RESTful and to integrate with 3rd party APIs
• Passionate user of Git/Bitbucket/Jira/Confluence.
• Experience with unit testing (jasmine, mocha, jest,etc.)
• Knowledgeable experience in Ruby-on-Rails, HTML5, CSS, JavaScript.

Qualification: Degree in computer science or a related subject or a sound education with practical experience in IT Software Industry.
​
Salary: Negotiable upon Experience.
​
Location: Bangalore, India.

Hello Everyone,
I'm a sophomore in an engineering college in India. Recently, there is a strong push to manufacture electronics especially PCB's in the country. I am brainstorming on some viable business ideas in PCB prototyping in India. There are certain advantages and quite a few disadvantages in manufacturing PCB's here. I would love to get ideas from the community on possible strategies as its not possible to copy China due to some constraints mentioned below.
Advantages in India :

1. Very cheap labour. In urban areas, you can get unskilled labour for Rs. 10000 a month i.e around $150 a month. Even less in rural areas. I was also talking to some of my friends in NGO's working on Women Empowerment in rural areas and they believe it would be great if women are able to earn money working on PCB assembly or other work that they can do in their homes. They are also open to the idea of training them in soldering etc.

This is a major advantage and get substitute large capital investment which I cannot make initially.

1. Availability of inexpensive engineers : There are alot of engineering graduates each year in India and most of them are from Tier 2/3 colleges. Average salary for fresh graduates starts from around $2500 per annum. Now, our education system is pretty shit and most of the students are nearly null practical knowledge as examinations can be cleared by ROTE learning. Yet, They are really good once proper hands-on training is provided. Overall, there are alot of young people looking for work here
   
2. English speaking population : Almost all of the educated Indians are able to understand English. There is less cultural/semantic barrier as most of them are pretty aware of western culture. This can possibly be advantageous as there is a communication barrier with chinese.
   

​
Disadvantages :

1. The Electronics supply chain in India is pretty shit. Almost all of the components barring few are imported from outside. There is no hardware ecosystem while the software ecosystem is one of the best in the world.
2. Challenges with expansion and automation are pretty real since almost none of the machines are manufactured locally eg. pick and place and other automated processes.
3. There is huge and unnecessary bureaucracy everywhere which hampers alot of ventures.

So overall, I don't think we can compete head on with JLCPCB etc because we can't get those very expensive machines and automated factory lines etc. In short, we dont have state of the art facilities. However, inexpensive labour and trained professionals (who are easily to communicate with) can open open certain niche opportunities.
I would love to know the possibilities from the community as most of you have experience with getting PCB's manufactured and assembled. Please share your experiences and ideas.
Thank You

I'm 27. I used to work back in my home country India 2 years back as a software developer. I left that job because they put too much load on me even before I was familiar with their technology. Fast forward and I'm working in a company in Kuwait where I found myself working as an assistant engineer who deals with automation. Apart from the position, there is nothing much to tag about in the salary. I barely make above the minimum wage people make here for the kind of job I do. There is nothing in this country you engage in. It's depressing. It's always the home-work-home cycle. Back in my home, my parents are alone too as I am a single child. I feel like going back to my own beautiful country and look for an opportunity (which is not so easy due to the high population) there to start working there and for once not feel like a slave working in a foreign land. I would also be able to learn new things and improve myself on my career front. Being in my current job, I don't think I'll be able to gain much knowledge as they are not innovative. But everyone who hears my plan dismisses it as something stupid and asks me to stay here in Kuwait. Their logic is that it's very difficult to find a job in India and even if you find one, it won't pay me as much as I get here. Well, they are not exactly wrong. But finding a job in India is not as easy as in other parts of the world. Especially when you have a surplus of qualified population. Yet, I have hope. My dream has always been to learn so much about technology and create something new and innovative for the world. So far, I have not been able to do that. And I strongly believe that I will be able to achieve that on leaving this job and going home. Working here is like being in a rat race. Trying to earn more. And working without any goals. If we want to achieve something which others haven't, we have to do something different. Otherwise, we'd take the same steps they took. The path to success is always the less taken one. It'll be full of hardships and twists. But only those who thread that path and finish succeed in their life. It's with this thought I have come to my decision. Do you have any experience of doing something similar? Quitting your job to chase your dreams? And what followed?

Going through the list of jobs from college till now:

• 6 months at a startup writing php, html, javascript, and jquery. (I left because sometimes the CEO didn't pay salary.)
• 1 year writing native python (no frameworks) and bash scripting. (I left because new CTO slowly replaced our team with his own people.)
• 2.5 years working in a consulting company, providing services for a proprietary old product called BMC Remedy. (I left out of fear of being pigeon-holed into a dying product.)
• 4.5 years working as an "automation engineer" which involved using proprietary software to building scripts that logged into machines, and ran shell scripts remotely. (I left because upper management slowly outsourced everything to India.)
• 6 months working for the federal government in a language called Ada. (I left because how little time we spent actually coding. I had fear of getting too used to the environment, and not being able to do anything outside of government work after that.)
• 1 years coding in Java. The only framework we use is GWT. Everything else is built in-house. A lot of my time is spent fixing those in-house modules. (I am forced to move soon for family reasons and I'm currently having trouble finding a job in the remote location.)

You can tell during my 10 years of experience, I've only had 1 year of recent Java exposure. I have almost no experience using frameworks. Most of my work involved proprietary software that isn't used elsewhere.
Up till now, I feel like I've gotten lucky, taking advantage of the great job environment in the US, that I was able to continue to switch jobs to different unrelated programming positions.
But now being forced to move during coronavirus, I've found it very hard to even get an interview for a junior position.. Even though I'm in a huge city.
It's not like I am a bad programmer.. I am good at programming design patterns.. Though frequently I don't get to use them as my recent work usually involves adding small features to other people's code.
I keep wondering what I should do to be able to get a job. The main thing I can think of, outside of applying more, is to work on some project in github. Though my favorite language is Rust, and even though contribute code there once in a while, I don't think it will help me much with my job search.
Am I just unlucky now because I'm applying during a hiring freeze? Or do I have a legitimate problem with my work history that I need to fix somehow? Should I try and get a master's‽

With the demand for Data Analysts rising with each passing day, it is significant for a person to possess a Data Analytics certification. A Data Analystcollects manipulates, and interprets the data, and transforms it into useful business strategies.
As the world faces development, it is significant for the firm to hire someone who can organize the trade’s progression. Many prominent institutions are aware of data Analytics’ significance and have initiated Data Science courses in Mumbai to meet industrial requirements.
What is Data Analytics?
Data Analytics is the process of converting raw data into actionable business insights. Data Analysis techniques have been automated into algorithms that are used over raw data for actionable insights.
Data Analysis techniques can reveal trends and metrics that can be lost due to an overload of information. This information can then be used to optimize processes to increase a business's overall efficiency.
Different Types of Jobs that Require Knowledge of Data Analytics
Before you take the time to learn a new skill set, you will likely be curious to know about the range of salaries in that particular domain. Knowing how your new skills will be rewarded gives you proper motivation for learning.
After completing a Data Analytics course, you can get a job both remotely and onsite. According to popular job search websites, here are some positions worth looking into – and their median salaries.

1. Business Intelligence Analyst

A Business Intelligence Analyst's most fundamental job is to find patterns — and value in their company data. At most companies, this is a kind of Data Analyst role. BI Analysts are expected to analyze data, work with SQL, and Data Visualization and modeling. Like most data roles, this job also requires strong communication skills to communicate your results convincingly to others involved in the process.
BI Analysts earn an average salary of $95,838 per year, plus an average $5,000 cash bonus.

1. Data Analyst

Data Analysts do precisely what the job title implies. They analyze the company’s data to find value and opportunities.
Data Analysts can be found in every industry, and job titles can vary. Some roles will have industry-specific names like Healthcare Data Analyst, Business Analyst, Intelligence Analyst, and similarly named roles that often share a lot with Data Analyst roles.
A Data Analyst's average salary is $75,253 per year, with an additional $2,500.

1. Data Scientist

Much like Analysts in other roles, Data Scientists collect and analyze data, to deliver actionable insights. Data Scientists are often a step above Data Analyststhough. They are the ones who can understand data from a more informed perspective to help make predictions. These positions require a strong knowledge of Data Analytics, including software tools, programming languages like Python or R, and Data Visualization skills to communicate the findings better.

1. Data Engineer

Data Engineers often focus on massive datasets and are tasked with optimizing the infrastructure surrounding different Data Analytics processes. For example, a Data Engineer might focus on capturing data to make an acquisition pipeline more efficient. They may also need to upgrade a database infrastructure for faster queries. These high-level Data Analytics professionals are also well-paid, with median salaries comparable to Data Scientists at $90,963.
The Scope of Data Analytics in India
Business Analytics in India has excellent prospects. A profession in Business Analytics is fulfilling and offers ample scope for learning and growth. Thorough knowledge of statistical techniques, quantitative capacity, business learning, logical thinking, instruments to understand the accessible data, and asset management are essential skills required to be a Business Analyst. Understanding business situations and problem-solving are other skills required.
About Boston Institute of Analytics(BIA)
Boston Institute of Analytics is an international organization that imparts training in predictive analytics, machine learning, and artificial intelligence to students and working professionals via classroom training conducted by industry experts. BIA is headquartered in Boston, USA, and has training programs across India with a mission to bring quality education in emerging technologies to the country.
BIA is driven by the industry’s top data science and analytics experts from across the world. Our team of experts and advisors has highly specialized knowledge across predictive analytics, machine learning, and artificial intelligence.
BIA courses are designed to train students and professionals on the industry's most widely sought after skills and make them job-ready in the field of data science.
With KPMG in India as a knowledge partner, BIA is offering a joint industry-oriented Data Science certification course with training sessions conducted by expert faculty and mentors from BIA and KPMG in India.
​

Originally shared on Facebook
I come from a small village in Bihar, lost my father at the age of 3 and then raised by my Mom. She worked as a tailor.
Mom wanted me to do at least graduation, but we couldn't afford it so at the age of 17, I left studies and came to Mumbai looking for a job.
In Mumbai I learned computer hardware & networking for 6 months and got a job at a MNC.
Technology fascinated me and I learned everything I could get my hands on, performed really well in the company and for the next 6 years I went from being a trainee to leading a team of 60 engineers, managing over 20 Lakh servers across 15,000 different customer locations.
Around 5th year in my job I realized I had more interest in building things on the internet than fixing active directory problems.
I started learning more and more about it, and built a blog which got decent visitors and started to make some money. But to really make something worthwhile I had to dedicate more time.
I used to live in Virar and traveling to Andheri (Mumbai) every day for a job took 9 hrs + 4 hrs of my time and after that hectic peak hour mumbai local trip the body simply didn't want to work anymore.
So in around 2013, right after my marriage, I left my job and decided to move full time into Digital marketing. My wife, her parents and my Mom all thought I have gone mad and pretty stupid. And honestly That's pretty much messed up if you ask any one of that age.
For the next 12 months I built websites, ranked them and made profits selling ads & leads. Soon enough, I was making over $10K a month with this. This was more than a year's worth of salary, every month!.
This boosted my confidence and I invested most of those funds into experimenting and investing in digital marketing. I used to post my screenshots on Facebook about my experiments, earnings etc.
In 2014, Suumit Shah (I didn't know him that tim) massaged me. After a few chats, I realised he was different. He was confident, full of energy and already doing fascinating stuff. I somehow liked him and connected well. Soon enough we met.
We discussed what we both had been doing. He had lots of contacts at emerging companies and I had the skills & resources to execute. It was a promising match and we ended up forming a company together.
We built a digital agency, the first client he brought gave us over two crores in revenue. We quickly built a team of about 40 ninjas and went on to become a very successful company.
In the next few years we worked with dozens of high growth startups, listed companies, even 3 fortune 500 companies and helped them really scale their growth. Companies we helped still get over 100M organic traffic a month.
This not only helped us realise the scale of internet economy, but also helped us learn exactly what works and what doesn't.
At this point we were already building technology to scale our work. For example, we automated building over 2000 websites, as well as crawling over 2M pages a day for reporting needs.
Later with the help of Kaustub Pandey and Anurag Meena we produtized our service as Rankz and crossed 1M ARR within a year.
Fast forward to 2020, We all have seen how hard Covid has hit India, specially small shopkeepers who earn their living everyday, with shops closed their livelihood were affected a lot and even putting food on table was difficult for them.
While india was quickly moving Digital and preferring all things online, these small shopkeepers were not really skilled enough to operate existing digital platforms like shopify / amazon etc.
Belonging from a shopkeeper's family @suumit saw the pain and wanted to do something about it. We discussed a solution and got on to build a platform for them - “The Dukaan App”.
Kudos to our ninjas Atul Dubey Dipen Bhatt Dawar Mir Anurag Meena for executing everything in just 2 days.
Dukaan is a simple mobile application, that enables shopkeepers or anyone else for that matter to quickly set up and run an online store. A typical Dukaandar now installs the Dukaan app, starts uploading inventory (as products), gets a unique store link, shares this link with his/her customers on WhatsApp and starts accepting online orders.
The app was a hit and just in 2 months, more than 2.7 Million small business owners are using the app to sell their products online.
From Small time grocery stores, to housewives selling their handmade items, to restaurants in small towns - these businesses are now getting thousands of orders every single day and able to sell online.
Today, Dukaan is backed by 2 of the most promising venture capitalists - Matrix Partners and LightSpeed Venture Partners as well as some of the most prominent entrepreneurs from india - Haresh Chawla (Founding CEO Network18,Viacom18), Ryan Hoover (Founder Weekend fund & Product Hunt), Jitendra Gupta (Founder Citrus Pay and Jupitor), Shashank Kumar (Co-Founder Razorpay), Sandeep Tandor (Founder Freecharge), Prabhkirna Singh (Founder Bewakoof), Kunal Shah (Founder Freecharge & Cred).
Within just 2 months we have helped these dukandar get over 600,000 orders and generate over Rs 100 crore in sales.
Together, we are trying to help the next 70 Million merchants go truly digital and the support from these awesome folks will help us get there. Faster!
https://www.cnbctv18.com/startup/dukaan-raises-6-million-in-seed-funding-co-led-by-matrix-partners-india-lightspeed-india-7240381.htm

I’ve been working as an Accountant for the better part of ten years and have some thoughts on the profession. First of all I got into the profession for financial stability. (Apparently a lot do) This can be furthest from the truth. My Company only seems to see value in revenue generating positions and tends to cut cost center positions. Wondering if others can speak to this ?
The amount of CPA’s that flow through the several companies I’ve worked for are staggering . I mean you can argue some people just might not be the right fit however it really comes down to companies valuing there employees and to be completely blunt I believe they do not value none revenue generating roles and are constantly looking to lean out labour costs with in them. This is a hard truth about the profession at least from an operations POV.
I would not recommend people pursue there CPA simply because the cost to benefit (to me) is not worth it. Unless for personal fulfillment or possibly lending credibility to achieve a promotion. I believe automation and an influx of new immigrants having greater access to learning hard skills ( accounting & Engineering ) are driving salaries to drop, fast.
As an example, my company has fired several senior accountants making 90k+ a year and hired new seniors fresh off the boat from India and are paying them roughly 55k a year. The role is exactly the same. I’m wondering if this is happening in other places and not just Ontario? Not to veer from the point but this is just another reason why I believe the profession is fizzling away.
Thoughts ?

This is an intro to anyone knew to Quality Engineering or Quality Assurance. While the /QualityEngineering sub-reddit is focused on more technical Quality Engineering related topics, this post for all quality related roles.
This is a work in progress - if you have suggestions, please leave a comment.

FAQ

What is the difference between a Quality Engineer (QE), Quality Assurance Engineer (QA/QAE), Quality Analyst (QA), Automation Engineer, Tester, Software Engineer in Test (SET), Test Engineer (TE), Software Development Engineer in Test (SDET), etc?
Unfortunately, there is no standardization of titles across companies, and companies are allowed to call any role by any title. Some companies will even purposefully misrepresent a role to lure candidates, eg calling a role an “Engineer” when all you’ll be doing is manually testing. Fortunately, this is rare. Here are some commonalities in job postings, but this is mostly limited to North America, Australia, and Europe.
SDET, SET, Automation Engineer, and QE are often more technical roles that require significant programming experience. SDET was originally coined by Microsoft to describe someone with production level development skills, but working on test engineering or automation problems. SET is a shortened version of this that was popularized by Google. QE is more general, but almost all roles using this term will require some level of programming.
QA/QAE are broad descriptors and can describe a very diverse set of roles, from non-technical to very technical. If you are applying to a QA position be clear about what the expectations are.
Automation Engineer usually used to describe someone who exclusively works on automation.
Test Engineer (TE) was coined by Google, and describes someone who mainly tests, but still understands technical concepts.
Quality Analyst and Tester are usually the least technical, and sometimes describe a purely manual, black-box role.
Some examples role descriptions:

1. Test Engineer in ProdEng at Google
2. Quality Engineer at Atlassian
3. Software Development Engineer - Test at Amazon

… rather than list more, put the title into a LinkedIn job search and see what comes out.
Is Quality Assurance / Quality Engineering a good career choice?
Yes, it is a career with growth opportunities, pays good money, allows you to learn on the job, is challenging, collaborative, and technical. Is it a good career for you? I can’t tell you that. It depends on what you are looking for, and what brings you joy.
If you like technology, enjoy challenges, like to deconstruct things, don’t mind working in an office environment, like working in collaborative teams, and are OK dealing with a little pressure, then it could be a great career for you. If you can’t stand the thought of sitting at a desk for 8 hours a day, maybe not.
How much money do QAs or QEs make? (also: Should I ask for more money? Am I underpaid?)
Money seems to be one of the most asked questions, second to “do I need to code”.

• Salary bands are very localized - what you make in San Francisco is not what you will make in Salt Lake City, and not what you will make in India.
• There is more to compensation than salary. Work-life balance, benefits (core and fringe), your interest in the specific type of work, the people you work with, your interest in the domain, and many other things will contribute to your enjoyment and fulfillment as a QA/QE. Don't lose sight of those because you are focusing on salary. In addition, the most important piece of your compensation, especially early in your career, is whether you are leaning on the job. Every day you spend working should make you more capable and thus more valuable. If this is not the case, look for employment elsewhere. From a career POV, the worst decision you can make is to work at a company where you will be exclusively using in-house, proprietary technology and tools.
• Don’t compare your salary to someone with the same title, and think you are underpaid, even if you are at the same company. This is a creative career, and two people with the same title might have significantly different abilities, and thus command different salaries.
• Senior Quality Engineers in tech hub cities in North America can easily make $100k-150k / year, with niche roles or roles at FANG-ish companies making significantly more. Non-technical, entry-level QA roles can start in the 40k range.

Do I need to know how to code?
There are still quality related roles that will not require you to code. There are fewer roles that don’t require you to code, AND don’t require an understanding of how software systems work ‘under the hood’. These jobs tend to be the lowest paid. Why limit yourself?
Learning to code is not a single, massive, one-and-done effort. It is a continual process that every coder continues for as long as they live. Don't be intimidated because there is so much out there that you think you can never master. Just start small and keep going.
Do I need a Computer Science or Computer Engineering degree?
Absolutely not. QA/QEs come from many backgrounds, from formal Computer Science/Engineering programs to self-taught. Each path has its strengths and weaknesses. Yes, a CS degree can give you an understanding of the theory behind software that you won’t get from bootcamps or elsewhere, but this isn’t required. In addition, much of the “theory” of computer science/engineering is also now available through free programs put on by universities themselves.
What certifications should I get?
Certifications are neither necessary nor sufficient to be competent as a QA/QE. However, the willingness to study for, take, and complete a certification does indicate a level of commitment that some employers find appealing. The most common and recognized certification is the ISTQB.In general, North American companies value certification less than European or Indian.
What does a QE/QA interview look like?
QE/QA interviews vary significantly between companies, the level of the role, and the type of software being developed.
What tools/frameworks/languages should I learn first?
This depends on what you are going for. Within North America, across a range of industries and cities, these are the most “in demand” tools I see for QE and related roles:
(within each category, tools are generally listed most in-demand to less in-demand)

• Project management tools: [Jira, Rally, Trello]
• CI/CD Orchestration tools: [Jenkins, Gitlab CI, Github Actions, AWS CodePipeline]
• Languages: [JavaScript, Java, Python, Typescript, C#]
• UI Frameworks: [Selenium, Cypress, Protractor, WebDriver.io]
• API Frameworks: [Postman, Insomnia, RestAssured (or equivalent), SoapUI]
• Cloud Provider [AWS, Azure, GCP]Misc: [Git, ChromeDevTools]

What tool/framework should I use to do X?
This changes so frequently, and is so dependent on the context of your use case, that you should just post directly.
Will software testers be replaced by AI bots?
It’s amazing how often this question comes up. Yes, there are many tool vendors leveraging AI/ML to solve specific problems within quality assurance and test automation. Many of these are hype and snake-oil, others provide legitimate value. There is a huge difference between these tools, and using AI to completely replace testers. Testing is a creative, abstract, contextual process that requires critical thought, judgement, assessment of human behavior, understanding of rist, etc.AI will augment and empower testers, not replace them. If/When it does replace testers, it will have already replaced everything else in the world, too.

Specific Resources recommended for all new QA/QE:

There are millions of resources/articles/blogs that explain topics within software, testing, and quality engineering. Many of these are half-baked, lower quality, or overly confusing. Finding a complete, well written, and concise article on an important topic can be a godsend to understanding. This is a highly curated list of resources of the highest quality, but necessarily reflects the moderator’s opinions. If you have a suggestion for something every new QA must read, leave a comment.
Testing vs Checking by James Bach\ https://www.satisfice.com/blog/archives/856
Atlassian’s take on Quality: Quality Assistance\ https://www.atlassian.com/inside-atlassian/quality-assurance-vs-quality-assistance
The Practical Test Pyramid\ https://martinfowler.com/articles/practical-test-pyramid.html
Microservice Testing\ https://martinfowler.com/articles/microservice-testing/
How Browsers Work\ https://www.html5rocks.com/en/tutorials/internals/howbrowserswork/
The Chrome Architecture Comic Book:\ https://www.google.com/googlebooks/chrome/

QE/QA Related Courses or Programs:

Applitools TestAutomationU\ https://testautomationu.applitools.com/
Khan Academy - Computing\ https://www.khanacademy.org/computing/
Black Box Software Testing (By Cem Kaner)\ http://www.testingeducation.org/BBST/
The Odin Project (for developers, great for understanding web applications)\ https://www.theodinproject.com/tracks

Large List of Additional Resources

This is just a dump of interesting, relevant, or topical information for a QA/QE.

Personal Blogs focused on Testing/QE/Automation:

Ordered by general notoriety - IE, most seasoned QE/QAs will know who James Bach is, very few will know the bottom of the list by name.

• James Bach Author, speaker: https://www.satisfice.com/blog
• Michael Bolton: Author, speaker: https://www.developsense.com/blog/
• James Whittaker: Author, speaker (Google, Microsoft): https://www.docjamesw.com/
• Lisa Crispin Author, speaker: http://lisacrispin.com/
• Janet Gregory Author, speaker: http://janetgregory.ca/
• Joe CaColantonio (Test Guild) : https://testguild.com/blog/
• Bret Pettichord Author, speaker: http://www.pettichord.com/
• Angie Jones: Author, speaker (Applitools): http://angiejones.tech/
• Paul Grizzaffi (Responsible Automation) https://responsibleautomation.wordpress.com/
• Eran Kinsbruner (Continuous Testing): https://continuoustesting.blog/
• Chris Kenst: https://www.kenst.com/
• Richard Bradshaw (The Friendly Tester): https://thefriendlytester.co.uk/
• Alan Richardson (Evil Tester): https://www.eviltester.com/blog/

Individual Blogs on general software topics

(...sometimes with good QE/QE/Testing/Automation content)

• Martin Fowler: https://martinfowler.com/bliki/
• Robert Martin (“Uncle Bob”) https://blog.cleancoder.com/

Communities:

• Stickyminds: https://www.stickyminds.com/ (owned by Techwell, but non-commercial)
• Ministry of Testing: https://www.ministryoftesting.com/softwaretesting
• /SoftwareTesting
• /QualityAssurance

Corporate Blogs (QA focused):

• Google: https://testing.googleblog.com/
• Microsoft: https://techcommunity.microsoft.com/t5/testingspot-blog/bg-p/TestingSpotBlog
• LinkedIn: https://engineering.linkedin.com/blog/topic/testing
• Atlassian Software Testing: https://www.atlassian.com/continuous-delivery/software-testing
• BrowserStack Blog: https://www.browserstack.com/blog/
• Applitools: https://applitools.com/blog/

Corporate Blogs (General Engineering, some QA/QE related content)

While it’s great to know how other successful companies are doing engineering, don’t get overwhelmed by the number here; you don’t need to read all of these. Just keep a few in mind for when you have 30 minutes on the bus and need to kill some time.

• Google: https://medium.com/google-developers
• Pivotal: https://pivotal.io/blog
• Puppet: (especially the State of DevOps report) https://puppet.com/resources/
• Thoughtworks: https://www.thoughtworks.com/insights/articles
• Netflix Technology Blog: https://medium.com/@NetflixTechBlog
• Medium Engineering: https://medium.engineering/
• SalesForce Engineering: https://engineering.salesforce.com/
• Expedia: https://medium.com/expedia-group-tech
• Slack: https://slack.engineering/
• Spotify: https://labs.spotify.com/
• AirBnB: https://medium.com/airbnb-engineering
• Pinterest: https://medium.com/@Pinterest_Engineering
• Slalom Build: https://medium.com/slalom-build
• 8thLight: https://8thlight.com/blog/

QA/QE Blog Aggregators

• https://www.ministryoftesting.com/feeds/blogs
• https://softwaretestingweekly.com/

Books:

[TODO... ]

I have been in tech startups for almost 10+ years now. Twice a founder. 2013-2017 I was founder & CEO at a 250+ strong online marketplace for blue collar workers in India. Raised $5 Million for that from Tiger and Lightspeed.
I have always been on the business side, but in 2019 I self learned to code, and have never felt more free! Launched two side projects, and now focusing full time on the latest one (Kaapi). Currently at $100 MRR..
A lot of my friends have been asking me about my coding journey, and I recently had a long Whatsapp chat on this topic, which I thought of compiling. It's a little biased towards startup founders, and by no means I am recommending that a coffee shop owner should learn code..
But there is probably a slight truth to the fact that systems building is a valuable skill in every role.

What superpowers does it give you?

Coding is definitely a commodity skill. But honestly the real power isn't coding. It's what code helps you do. To build and solve problems.

There are so many no code tools out there now where you can build things without code. You can literally build an Airbnb style marketplace in 2 hours now via Webflow.

But knowing how to code will make your no-code experience 100x. I personally absolutely enjoy it. And it has made me a better product person. I know the pains a business request will give to the tech. I know which one is genuinely easy. I can attract talent better because I don't have to depend on someone to ship the MVP out. I can build it, get users, and then attract amazing engineers. It is absolutely a superpower. I can just get started and validate things faster

Would I still do it if I could raise VC money to hire engineers?

Absolutely yes. I did it initially because I knew I wouldn't have the money, but now I have come to believe that everyone should know this. I spent 1.5 years of weekend and nights optimising for my current life of running a micro-SaaS. But if you are in anyways involved in tech startups, you should learn how to code too.
Some should be experts at it, and build a career, but at a general level everyone should do it. If we ever hire people now at Kaapi, I will most probably have a criteria that they must push some code out regularly.

Why should startup founders learn code

Earlier, as a non-tech founder, I was doing manual stuff for way too long. I think I went too far the spectrum on sales first. I still do sales first without coding, but I know what I am getting into. Estimates are better. Throwing people at problem doesn't work. Slow down, breathe and throw a code (even a hacky script is better than hiring a person) at the problem.
This approach has been serving me way better. Me and my part-time cofounder are achieving things that are usually done by 2 founders, 1 PM, 1 designer, and 2 engineers.
Today I know that if I can get to 1000$ MRR with just me and a [art time cofounder, then we are sorted. The base would be very strong because everything will be automated. And growing will be a matter of when and not if.. If I want to get an investment, it will come to me. My destiny in my own hands is very much doable.

It helps you get ramen profitability easier. And build optionality.

It's simple math honestly. If you can code yourself, then you can definitely get 1000$ MRR without worrying too much about marketing, GTM and other stuff. The internet is just too vast. You can build the most niche product and still get to 1000$ MRR ramen profitability. But if you are 4 cofounders, you now have to hit 10x-12x that. Because then you need an office, taxes, and some early employees will need a salary etc etc. You have already decreased your odds of finding validation and success.

tl;dr

You don't need to know code to be successful, but knowing code will increase your chances of success 10x. But as always, it's never a one fit answer. This philosophy probably can not be copy pasted if you are building Uber, or Facebook. Maybe it works great for building SaaS.
Would love to know what you think! Am I being too extreme in thinking like this? 🧐🤓

Hello!
I am Ayush Sharma, a multidisciplinary fullstack developer who loves the intersection of art and code. I have a Bachelor's in EEE from BITS Pilani University and I work as a digital nomad to make rad, fun little apps as a hobby and to make a living.
I'm looking for a remote job as a Generalist Developer. Job security is important to me so if the offer and place suits me, I'm open to relocation as well.
In my career so far, I have worked with 2 mid-level companies, over 7 distributed remote startup teams and US/Canada based clients on their products and have a history of having taking them from an idea to MVP while working as a fullstack engineer. I have also built web scrapers, data visualizations & web automation bots (Tinder, Whatsapp, Facebook, Makemytrip, etc.) to simplify every day business tasks of my clients.
I understand the importance of communication and constant feedback loops and updates in remote collaboration after having worked remotely with so many different people. I send my clients screencasts (video updates) at regular intervals (usually 3 times a week for frontend, less for backend). So if you hire me, I will make all your dreams come true :P
Stack: Though I have no doubt that I can pick up any stack really quickly, I'm currently in momentum with Vue (or Gridsome/SabeVuepress, etc.), React (or Gatsby), Typescript, WebGL, p5.js, Three.js, Canvas API, D3, Tensorflow.js, WebAudio, SEO, Web Accessibility, Node JS and Python (Django, Flask), Data Science tooling (Pandas, Selenium, Scrappy, Tensorflow, etc.), Bash.
Salary Expectations: 40 USD an hour (or preferred: fixed 4.5k+ USD per month with a bonding period)
Bonding Period: Atleast 3 months.
Portfolio: ayushsharma.net/portfolio.
Email: [[email protected]](mailto:[email protected])
I would insist that you gauge my potential by having a look at some of my passion projects that are up online (shameless self promotion). Following are some open-sourced "teaching aids" that I have been making as hobby projects for a while now. Some schools in India use these to teach physics concepts :-
Bird flocking algorithm that follows the mouse.
Visualisation of Mathematical Operations on two Waves (WaveOps)
Illusion of 3D with Circles
For Physics dudes:
Exp. #67 : Dipole & Magnetic Particles
Exp. #68 : Young’s Double Slit Experiment
Exp. #5 : Lissajous Figure Creator
Exp. #66 : 2 Stars & 200 Particles
Exp. #61 : SHM : Spring, Bob & Hinge System
Exp. #54 : EMT - Ring & Point Charge System
For CS dudes:
Exp. #136 : Algoviz : A* Search
Exp. #58 : Neural Network Viz #2
Exp. #107 : Maze Generation #1 : Recursive Backtracking
For Matheletes:
Exp. #19 : Spiral Pattern Implementation #1
Exp. #62 : Mathematical Flowerinator
Exp. #17 : Gaussian Distribution of Points
Random educational stuff:
Exp. #104 : Extracting Simpler Oscillations
Total collection including some other stuff that I have done (github repository) :
Exp. #1 : Illusion with Circles #1
Exp. #2: WaveOps
Exp. #3 : Warp Drive
Exp. #4 : Bouncing Balls
Exp. #5 : Lissajous Figure Creator
Exp. #6 : Fractal Implementation #1 : Tree
Exp. #7 : Orbital Motion : 2D implementation
Exp. #8 : Graph Paper - Customizable
Exp. #9 : Illusion of 3D with Squares
Exp. #10 : Particle : Cloud-ish Effect
Exp. #11 : Rotating 3D Cube (Primitive)
Exp. #12 : Fractal Implementation #2 : Squares over Squares
Exp. #13 : Analog Clock Implementation #1
Exp. #14 : Sound Visualizer Implementation #1
Exp. #15 : Vector Field : 2D
Exp. #16 : Animation inspired from Crop Circle pattern
Exp. #17 : Gaussian Distribution of Points
Exp. #18 : Data Viz #1 : Pi upto 500 decimal places.
Exp. #19 : Spiral Pattern Implementation #1
Exp. #20 : Slightly Useful Typewriter
Exp. #21 : Fractal Implementation #3 : Circles
Exp. #22 : Dussehra 2017 - 2D Fireworks.
Exp. #23 : Spooky Eyes
Exp. #24 : Mouse Torch
Exp. #25 : 3D Oscillations #1 - Cubes
Exp. #26 : Packing : Circles
Exp. #27 : Illusion with Circles #2
Exp. #28 : 10 PRINT Pattern Implementation #1
Exp. #29 : Fractal Implementation #4 : Arcs
Exp. #30 : Deception with Colours #1
Exp. #31 : Yin Yang
Exp. #32 : 2 dimensional iterative animation #1 : Maze
Exp. #33 : Sound Visualizer #2 : Bars
Exp. #34 : Pixel Data Manipulation #1 : Dance!
Exp. #35 : Cube Layers.
Exp. #36 : Fractal Implementation #5 : Cubes
Exp. #37 : 2D Shape Customizer
Exp. #38 : Fractal Implementation #6 : Suits Animation
Exp. #39 : Algorithmic Botany : Tree v1
Exp. #40 : Sound Propagation : Compression & Rarefaction Animation
Exp. #41 : Happy Diwali!
Exp. #42 : Mirrored Drawing Pad
Exp. #43 : The Matrix Terminal
Exp. #44 : Illusion with Circles #3
Exp. #45 : Illusion with Squares #2
Exp. #46 : Illusion with Circles #4
Exp. #47 : Illusion with Polygons : Hexagon #1
Exp. #48 : Illusion with Circles #5
Exp. #49 : Rotating Rectangular Brush
Exp. #50 : ½ Century
Exp. #51 : Abstract Geometrical Art #1
Exp. #52 : Abstract Geometrical Art #2
Exp. #53 : Noisy Plane
Exp. #54 : EMT - Ring & Point Charge System
Exp. #55 : Abstract Geometrical Art #3
Exp. #56 : Deception with Colours #2
Exp. #57 : Neural Network Viz #1
Exp. #58 : Neural Network Viz #2
Exp. #59 : Seizure Inducing Illusion
Exp. #60 : Fractal #7 : 3D Vicsek
Exp. #61 : SHM : Spring, Bob & Hinge System
Exp. #62 : Mathematical Flowerinator
Exp. #63 : Oscillating Sliders
Exp. #64 : Algorithmic Botany : Phyllotaxis
Exp. #65 : 3D Sound Visualizer
Exp. #66 : 2 Stars & 200 Particles
Exp. #67 : Dipole & Magnetic Particles
Exp. #68 : Young’s Double Slit Experiment
Exp. #69 : Abstract Geometrical Art #4
Exp. #70 : Fractal Spirograph v1
Exp. #71 : Algorithmic Botany : Trees #2
Exp. #72 : ‘Trend’ Line Calculator
Exp. #73 : Sound Viz - Mickey Mouse - Hot Dog
Exp. #74 : Menger Sponge Fractal
Exp. #75 : Schematic Diagram of a DC Machine
Exp. #76 : Stars and Particles : v2
Exp. #77 : 2D Rain Simulation
Exp. #78 : Metaballs / Isosurfaces in 2D canvas
Exp. #79 : Iterative Sketching #1 : Sun & Moon
Exp. #80 : Mountain Landscape
Exp. #81 : Abstract Geometrical Art #5
Exp. #82 : Geometry Viz : Area of Δ
Exp. #83 : Particle Beanstalk
Exp. #84 : A Globe
Exp. #85 : Mouse Seekers #1
Exp. #86 : Metaballs / Isosurfaces v2
Exp. #87 : Crazy Cells
Exp. #88 : Pixel Data Manipulation #2 : Ascii Art
Exp. #89 : Abstract Geometrical Art #6 : Hex Nuts
Exp. #90 : Iterative Sketching #2 : Furry Smoke
Exp. #91 : Iterative Sketching #3 : Stain
Exp. #92 : Arbitrary Sketch #1
Exp. #93 : Arbitrary Sketch #2
Exp. #94 : Squiggly Waves of Sun
Exp. #95 : Mouse Seekers #2
Exp. #96 : Iterative Sketching #4 : Fire Pit
Exp. #97 : Shape Morphing #1 : ▲ to ⬤
Exp. #98 : Shape Morphing : + Controls
Exp. #99 : Artificial Life : Flocking Agents #1
Exp. #100 : Artificial Life : Path Following Bots
Exp. #101 : Arbitrary Sketch #3 : Hypnotic Iris
Exp. #102: Collision Detection : Particles
Exp. #103 : Koch Snowflake
Exp. #104 : Extracting Simpler Oscillations
Exp. #105 : Psychedelic Noisy Vectors
Exp. #106 : JS Reserved Keywords (2017)
Exp. #107 : Maze Generation #1 : RB
Exp. #108 : Conway’s Game of Life v1
Exp. #109 : Metaballs / Isosurfaces v3
Exp. #110 : Abstract Geometrical Art #7 (3D)
Exp. #111 : Iterative Sketching #5 : Solar Flare
Exp. #112 : DNA : Double Helix (3D)
Exp. #113 : Abstract Geometrical Art #8 (3D)
Exp. #114 : Artificial Life : Flocking Agents #2
Exp. #115 : Artificial Life : Flocking Agents #3
Exp. #116 : Abstract Geometrical Art #9 (3D)
Exp. #117 : Abstract Geometrical Art #10 (3D)
Exp. #118 : Abstract Geometrical Art #11 (3D)
Exp. #119 : Electromagnetic Wave Propagation
Exp. #120 : Abstract Geometrical Art #12 (3D)
Exp. #121 : Abstract Geometrical Art #13 (3D)
Exp. #122 : Abstract Geometrical Art #14 (3D)
Exp. #123 : Abstract Geometrical Art #15 (3D)
Exp. #124 : Color Spray!
Exp. #125 : Color Explosion!
Exp. #126 : Abstract Geometrical Art #16
Exp. #127 : Immortal Snake Adventures
Exp. #128 : Algorithm Visualizaiton : TSP - I
Exp. #129 : Ripple
Exp. #130 : Rainbow Rain
Exp. #131 : 7 Segment Display
Exp. #132 : Stack Overflow 3D
Exp. #133 : Valentine’s Day
Exp. #134 : Spheres on a Sphere
Exp. #135 : Pixel Tunnel
Exp. #136 : Algoviz : A* Search
Exp. #137 : Jelly Fish Prototype
Exp. #138 : Celebratory Explosions
Exp. #139 : Life Spreading Brush
Exp. #140 : Artificial Life : Cockroaches
Exp. #141 : Evolutionary Rockets
Exp. #142 : Connected Spring Mass System
Exp. #143 : Video Pixel Manipulation
Exp. #144 : Football Field
Exp. #145 : Video Pixel Manipulation 3D
Exp. #146 : Unstable Colorful Spirits
Exp. #147 : Squiggly Life*
Exp. #148 : 3D Oscillations : Spheres on Sphere
Exp. #149 : 3D Oscillations : Black Hole Effect
Exp. #150 : Wind
Exp. #151 : 3D Oscillations: Rectangular Sheets
Exp. #152 : Poisson Disc Sampling
Exp. #153 : Radially Shrinking Superellipses
Exp. #154 : Animated Cochleiod
Exp. #155 : Hexaskelion
Exp. #160 : Liquid Simulation (Interactive)

This entire information is aimed at both the beginners & the people interested in data science either for changing field or curiosity purposes; seasoned professionals feel free to contribute more, I appreciate advice to improve all the information and material collected here.

What Data Science is and is not

Data Science is not exactly a new profession, it has been around for quite some time & we've seen it grow crazily over the recent years all of a sudden, with high demand and crazy salaries offered by big companies, influencers advertising it as the best career option, huge number of MOOCs as well as college courses. The Advent of such high demand is in line with the machine learning/deep learning craze and companies wanting to harvest technologies for more profit.
So, we are advertised that with some machine learning skills & a little bit of programming, we can be earning a ton and living the dream, seems too good to be true, isn't it? Just take some large dataset, run it through a little bit of Python and Bam! profit. Let me stop you right there and crush these kinds of dreams, because this is a huge pile of bullshit.
First, the kind of machine learning we come across at various MOOCs claiming to teach you data science (or even kaggle for that matter), are absolute opposite of the actual kind of work you'll be doing. While you glance at the profession with rose tinted glasses, stained by the attractive packaging and advertisement of the role, don't forget the second word in the name itself: SCIENTIST
Second, the company is investing huge resources because they're betting on an even better return, and you've to bear the burden of it. They're investing to extract value from things they aren't able to see, and this isn't just the entirety of it, you've got a lot more stuff.
Here's a bit of reality: Machine Learning is less than 10% of the part of the job. Don't think you'll just be running new models every week and just sipping on your coffee while you watch it train and then just do the magical sklearn.metrics.accuracy_score(y_train, y_test) , get 0.97665 and be paid lakhs for it.
The advertising is all kinds of bullshit, which has started to fill the field up with people who don't understand what the job is, let alone provide any kind of ROI, in the end just wasting resources of the company. If you think you can learn about it on the job, then drop the idea immediately. If you think you will just have some fancy tech and write small bits of easy python code, then please, by all means, this field isn't for you. And for the last time, do not listen to the advertisement.

The Process & Work

So, let's get into the jucier bits, after all, what exactly is the work I talked about for so long?
With the huge investment the companies put into Data Science, think of them as a valueable client who believe you can give them valueable returns in the end. How would you like paying an Android developer over 2 lakhs just to get a template with few edited strings? It's the very same thing with this profession; imagine a company putting up a handsome 18lpa package, you get through the selection, and at the end Surprise! you can't make sense of anything despite clear explanation or documentation, you wonder where to put up a Scikit-learn model, or hell, how do you even make sense of the data you've to pass through it, you don't have a clearly formatted CSV or sequentially laid out images pre-curated for you.
The largest part of your work, as well as the only thing you will & are supposed to learn on work, is understanding the data and how it relates to your domain. Your 2 major questions associated with it are, how do we make use of this to positively attract the customers? and why are certain parts more important than the others in making a decision? Sounds like computerised marketing & management? Because yes, a lot of it is.
A large chunk of your time will be spent automating the stuff to ensure your work goes smoothly or even for other people. Your work is providing solutions that can improve the ROI throughout. Expect to be building scrapers, pipelines, databases, servers, computing clusters and more such stuff for the majority of your time, just to ensure that when you do your actual work, it goes way faster than it would've traditionally. If your entire skillset consists of Python & ML, trust me, you'll struggle a lot & it's because of exactly this, many recruiters put of absurd filtering mechanisms to weed out the said candidates, which sometimes equally backfire on geniuine ones.

Skills to Learn

Get this into your head Data Science is not machine learning and vice versa is also true.
Your first step into Data science isn't python programming, it's data. Learn how data moves or is stored, how do you fetch data, how it is structured. Start with the most popular form of data storage, as well as the easiest one to use: SQL.
Here's a few good sources:

• W3schools
  
• Official Postgresql doc
  

Once you're done with these, I recommend learning a programming language(obviously python is the preferred choice, followed by R), and then do the operations through scripts. There's a lot of good tutorials out there, just remember, you'll learn more by doing on your own than watching a video and memorizing syntax.
Moving onto the more interesting(or boring if that's not your thing), learn to manipulate the data and how things work together in it. Pandas has an amazing documentation with examples and pretty comprehensive set of functions to get you started(yup, this isn't going to be your run off the mill make dataframe, make prediction). This is what will help you to make your data manipulation job easier (no, there is not a library to clean up your data, because everyone has a different requirement and certain things can be pretty sensitive despite appearing opposite). You'll build on this & numpy, to optimize your operations. This will be a pretty headbanging part with having to keep track of the flow, types, deltas and many more such stuff relevant to your requirement, but remember this will be one time, and when your data updated, you've got this super easy command in your terminal looking like "python3 cleaner.py" and you can just forget about having to go through the headache again. So make sure to understand all this too. This is your software engineering part. Oh, don't forget the management's archiac but still efficient tool of choice, the software of the gods: MS freaking Excel(yes, unfortunately, you'll also be doing your work in here as well)
You'll also have to create pipelines & scrapers to ensure your flow of data is efficient and just how you need it. You're going to have to learn about web, crawlers, bots etc. This is the dirty part which no one actually tells you about. Python as your sole programming language won't suffice here. Yes, you've got selenium, but honestly how much functionality does it really provide? For complete efficiency as well as ease, you're going to have to understand web technologies, stuff like browsers, how crawlers work, backend tech like Flask, Django or Node.
Here's a few good resources:

• Corey Schafer
  
• Sentdex
  
• Freecodecamp
  

But but, MaChInE lEaRnInG
Here's the thing, yes, ML is pretty important, but also, a lot of times it won't be part of your project. Your main work is solving the problems to increase efficiency, wherein you might even come across some colleague complaining about something & you automate it for them. Your package includes this work as well, in the end you're boosting the productivity in workflow, providing the return of investment by the employer. Learn to design stuff & think of solutions. Ask yourself, how can I improve what I see in front of me, or what I hear around me.
Here's a little bit of salt, some of your DS projects will ignore programming standards in favor of quick delivery. You might just end up watching from datetime import * (I can already hear angry hordes of software engineers with their pitchforks on my door, send help).
u/VeTech16 has written a great post about Machine Learning. It includes a large part of the mathematics required for both ML as well as data science.
Most important among all:

LEARN & UNDERSTAND STATISTICS AND PROBABILITY

Seriously, any amount of emphasis on this statement is not enough. Between all the programming stuff, before you go about just creating code, the most important thing is to measure the impact and if it's worth the resources utilized. You form a hypothesis based on data you gather, it can be any form, even overhearing junior management complaining about having to go through filling in the pesky Excel columns. Your first step is forming a hypothesis. Let me take the very same example and create a project right here:

• Overhear management talking about low turnover for a type of customer with certain parameters
  
• Hypothesis: Customer needs a push in form of incentives
  
• Weighing in with statistics: How significant is the customer type to the business? Why should I want to attract them? Does my hypothesis fit the problem?
  
• I design an algorithm to detect these type of customers and provide them with said incentive.
  
• 2 possible outcomes: It works, customer turnover improves. Does not work, hypotheses is flawed.
  

You don't want to be having the second result after writing the algorithm, since it can even lead to a good amount of loss depending on the traffic. Which is why stats is necessary, so that you can answer the three questions asked and alter your hypothesis accordingly until you see a net positive outcome, after which you create an algorithm for the same.
Yes, you'll definitely create Machine Learning models and predict stuff. There's way too many courses on this subject and some of my personal favorite have been: Andrew NG at Coursera, Fast.ai and Sentdex at YouTube(given above). These are good enough to get you started, but in actual scenario you'll need a deeper knowledge since you'll have to explain why you did what you did, otherwise it's almost like your MOOC with trying out random stuff to see which fits best. You won't get to do random stuff at work, you decide & choose a model or 2, or create a pipeline with multiple of them. Understand the maths behind algorithms and the when & why to choose them. Sometimes your result can be obtained even without having to create any ML model at all, so keep this in mind as well. If you're so much wanting to work on the cool looking DL stuff with cutting edge tech, just enroll in academia or R&D.
I did talk about distributed computing. I'm not too familiar with it myself, but I would suggest learning atleast something about it, you won't encounter it much in startups, but big corporates with Terabytes of data would be using them.
Cloud computing like AWS & Azure is equally important skill with a lot of takers. Learn how to utilize the various infrastructures offered and try to couple them together to your advantage.
Lastly, if you want to get into Data Science geniuinely, go for it, it's fun despite the heavy responsibility if you understand the stuff, just understand that it takes both patience and an analytical mind. Not everyone has the talent or complete interest for the real work involved, but there are so much other options as well, so really, just work hard and if things don't work out, you still got to learn a lot, which can be applied elsewhere as well.

The Next Big Thing to look up onto is Python and there is no doubt about that. Questions related to its worth, career opportunities, or available jobs are not to be worried about. As Python is rapidly ceasing the popularity amongst developers and various other fields, its contribution to the advancement of your career is immense.
There are reasons why Python is “the one”. It is easily scripted language that can be learned quickly. Hence reducing the overall development time of the project code. It has a set of different libraries and APIs that support data analysis, data visualization, and data manipulation.

Python Career Opportunities

Number of Python Jobs

While there’s a high demand for Python developers in India, the supply is really, really low. To testify this, we’ll take account of an HR professional statement. The professional was expected to recruit 10 programmers each for both Java and Python. About a hundred good resumes flooded in for Java, but they received only 8 good ones for Python. So, while they had to go through a long process to filter out good candidates, with Python, they had no choice but to take those 8 candidates.
What does this tell you about the situation? Even though Python has easy syntax, we really need more people in India to upskill themselves. This is what makes it a great opportunity for Indians to get skilled in python. When we talk about the number of jobs, there may not be too many for Python in India. But we have an excellent number of jobs per Python programmer.
Job boards like Indeed and Naukri offer around 20,000 to 50,000 job listings for Python and this shows that Python career opportunities in India are High. Choosing Online Python Classes in Lucknow to pursue your career is a good choice. The below stats shows the total job postings of the major programming languages.

Types of Python Jobs

So what types of jobs can you land with Python?
Well, for one, Python scope is intensive in data science and analysis. Clients often want hidden patterns extracted from their data pools. It is also preferred in machine learning and artificial intelligence. Data scientists love Python. Also, in our article on applications of Python, we read about how Python is used everywhere in web development, desktop applications, data science, and network programming.

Python Job Profiles

With Python on your resume, you may end up with one of the following positions in a reputed company:

1. Software Engineer

· Analyze user requirements
· Write and test code
· Write operational documentation
· Consult clients and work closely with other staff
· Develop existing programs

2. Senior Software Engineer

· Develop high-quality software architecture
· Automate tasks via scripting and other tools
· Review and debug code
· Perform validation and verification testing
· Implement version control and design patterns

3. DevOps Engineer

· Deploy updates and fixes
· Analyze and resolve technical issues
· Design procedures for maintenance and troubleshooting
· Develop scripts to automate visualization
· Deliver Level 2 technical support

4. Data Scientist

· Identify data sources and automate the collection
· Preprocess data & analyze it to discover trends
· Design predictive models and ML algorithms
· Perform data visualization
· Propose solutions to business challenges

5. Senior Data Scientist

· Supervise junior data analysts
· Build analytical tools to generate insight, discover patterns, and predict behavior
· Implement ML and statistics-based algorithms
· Propose ideas for leveraging possessed data
· Communicate findings to business partners

Python Future

While many top companies are stuck with Java, Python is one of the old yet trending technologies. The future of Python is bright with :

1. Artificial Intelligence

Artificial Intelligence is the intelligence displayed by machines. This is in contrast to the natural intelligence displayed by humans and other animals. It is one of the new technologies taking over the world. When it’s about AI, Python is one of the first choices; in fact, it is one of the most-suited languages for it.
For this purpose, we have different frameworks, libraries, and tools dedicated to letting AI replace human efforts. Not only does it help with that, but it also raises efficiency and accuracy. AI gives us speech recognition systems, autonomous cars, etc.
The following tools and libraries ship for these branches of AI:
· Machine Learning – PyML, PyBrain, scikit-learn, MDP Toolkit, GraphLab Create, MIPy
· General AI – pyDatalog, AIMA, EasyAI, SimpleAI
· Neural Networks – PyAnn, pyrenn, ffnet, neuro lab
· Natural Language and Text Processing – Quepy, NLTK, genism

2. Big Data

Big Data is the term for data sets so voluminous and complex that traditional data-processing application software is inadequate in dealing with them.
Python has helped Big Data grow, its libraries allow us to analyze and work with a large amount of data across clusters:
· Pandas
· scikit-learn
· NumPy
· SciPy
· GraphLab Create
· IPython
· Bokeh
· Agate
· PySpark
· Dask

3. Networking

Python also lets us configure routers and switches, and perform other network-automation tasks cost-effectively. For this, we have the following Python libraries:
· Ansible
· Netmiko
· NAPALM(Network Automation and Programmability Abstraction Layer with Multivendor Support)
· Pyeapi
· Junos PyEZ
· PySNMP
· Paramiko SSH
All these technologies rely on Python today and tomorrow.

Top Organizations Using Python

With its extreme popularity and powerfulness, Python is preferred by unicorns too:

1. NASA & ISRO

NASA and ISRO use Workflow Automation System (WAS), an application written and developed in Python. It was developed by NASA’s shuttle-support contractor USA (United Space Alliance).
NASA also uses Python for APOD (Astronomy Picture Of the Day), API, PyTransit, PyMDP Toolbox, EVEREST.

2. Google

Who, on this Earth, lives and doesn’t know Google? We use it for everything – sometimes, even to find answers to life’s deepest questions. Google uses Python for its internal systems, and its APIs for report-generation, log analysis, A/Q and testing, and writing core search-algorithms.

3. Nokia

This one reminds me of Nokia 3310, the pocket phone that could break a tile. Nokia makes use of PyS60 (Python for S60). It also uses PyMaemo (Python for Maemo) for its S60 (Symbian), and Maemo (Linux) software platforms.

4. IBM

An American multinational technology company headquartered in New York, IBM uses Python for its factory tool control applications.

5. Yahoo! Maps

Maps is an online mapping portal by Yahoo! It uses Python in many of its mapping lookup services and addresses.

6. Walt Disney Feature Animation

WDFA uses Python as a scripting language for animation. All the magic that happens in Disneyland has a bit of Python behind it.

Why Python?

So, after all this Python career opportunities talk, why should you take Online Python Classes in Lucknow? What has it to offer to you? What’s the scope of Python? Let’s see.
· Its simplicity and conciseness make it perfect for beginners.
· It has a large community that continuously contributes to its development.
· Because of the highly demand-supply ratio, it provides excellent career opportunities, especially in India.
· We have a number of frameworks to make web development easy as pie.
· Python is the preferred language for Artificial Intelligence and Machine Learning.
· Raspberry Pi, a microcomputer, lets us make our own DIYs with Python, at prices that do not blast holes in your pockets.
· Both startups and corporates, make extensive use of Python, thanks to its powerfulness and simplicity.
· Python has been consecutively topping the most loved programming language on the StackOverflow developers survey report.
· StackOverflow survey reports showed us that Python is the fastest growing language in high-income countries. IBM used the STL model to predict the future growth of major languages in 2020 and it seems Python is going to leave everyone behind.

Why is Python in demand?

According to expert research, there is a huge gap between demand and supply of python developers/experts across countries like India, the USA, and more. As a result, the available python developers are paid thrice of that of actual salaries to fill the scarcity. This is an important lesson for all those who are doubting the career opportunities with python and also lacking a good hold in python. Expertise in python by gaining experience or even through online python certification training. It adds value to your resume and all-in-all to your overall career goal.

Python Skills

After knowing all the opportunities that Python holds, its good to know all the ins and out to it. Focus is always on skill first so that you stand out amongst others. They can be broken down as follows:
· Core Python (Basic knowledge between Python 2 and Python 3 is sufficient, complete knowledge of all modules is not required)
· Web Frameworks (Learn common Python frameworks such as Django or Pandas)
· Object-relational mappers (Ability to connect to the database with the help of ORM rather than SQL )
· Understand Multiprocess Architecture (Ability to write and manage threads for high-performance)
· RESTful APIs (understand how to use them and able to integrate components with them)
· Building Python Applications (One should know how to package up a code and deployment and release)
· Good communication and designing skills (Able to communicate well with members as well as implement servers that are scalable, secure and highly available)
This was all in the Python career opportunities article that provides you benefits by taking Online Python Classes in Lucknow.

Hello!
I am Ayush Sharma, a multidisciplinary fullstack developer who loves the intersection of art and code. I have a Bachelor's in EEE from BITS Pilani University and I work as a digital nomad to make rad, fun little apps as a hobby and to make a living.
I would insist that you gauge my potential by having a look at some of my passion projects that are up online (shameless self promotion). Following are some open-sourced "teaching aids" that I have been making as hobby projects for a while now. Some schools in India use these to teach physics concepts :-
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Artificial Life - Bird Flocking Algorithm
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Illusion with Circles
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Mathematical Operations on Two Waves - WaveOps
​
In my career so far, I have worked with 2 mid-level companies, over 7 distributed remote startup teams and US/Canada based clients on their products and have a history of having taking them from an idea to MVP while working as a fullstack engineer. I have also built web scrapers, data visualizations & web automation bots (Tinder, Whatsapp, Facebook, Makemytrip, etc.) to simplify every day business tasks of my clients.
I'm looking for a remote job as a Generalist Developer. Job security is important to me so if the offer and place suits me, I'm open to relocation as well.
I understand the importance of communication and constant feedback loops and updates in remote collaboration after having worked remotely with so many different people. I send my clients screencasts (video updates) at regular intervals (usually 3 times a week for frontend, less for backend). So if you hire me, I will make all your dreams come true :P
Stack: Though I have no doubt that I can pick up any stack really quickly, I'm currently in momentum with Vue (or Gridsome/SabeVuepress, etc.), VueX, React (or Gatsby), Redux, MobX, Typescript, WebGL, p5.js, Three.js, Canvas API, D3, Tensorflow.js, WebAudio, SEO, Web Accessibility, Node JS and Python (Django, Flask), Data Science tooling (Pandas, Selenium, Scrappy, Tensorflow, etc.), Bash.
Salary Expectations: 40 USD an hour (or preferred: fixed 4.5k+ USD per month with a bonding period)
Bonding Period: Atleast 3 months.
Portfolio: ayushsharma.net/portfolio.
Email: [[email protected]](mailto:[email protected])
Here are some other things that I've created:
For Physics dudes:
Exp. #67 : Dipole & Magnetic Particles
Exp. #68 : Young’s Double Slit Experiment
Exp. #5 : Lissajous Figure Creator
Exp. #66 : 2 Stars & 200 Particles
Exp. #61 : SHM : Spring, Bob & Hinge System
Exp. #54 : EMT - Ring & Point Charge System
For CS dudes:
Exp. #136 : Algoviz : A* Search
Exp. #58 : Neural Network Viz #2
Exp. #107 : Maze Generation #1 : Recursive Backtracking
For Matheletes:
Exp. #19 : Spiral Pattern Implementation #1
Exp. #62 : Mathematical Flowerinator
Exp. #17 : Gaussian Distribution of Points
Random educational stuff:
Exp. #104 : Extracting Simpler Oscillations
Total collection including some other stuff that I have done (github repository) :
Exp. #1 : Illusion with Circles #1
Exp. #2: WaveOps
Exp. #3 : Warp Drive
Exp. #4 : Bouncing Balls
Exp. #5 : Lissajous Figure Creator
Exp. #6 : Fractal Implementation #1 : Tree
Exp. #7 : Orbital Motion : 2D implementation
Exp. #8 : Graph Paper - Customizable
Exp. #9 : Illusion of 3D with Squares
Exp. #10 : Particle : Cloud-ish Effect
Exp. #11 : Rotating 3D Cube (Primitive)
Exp. #12 : Fractal Implementation #2 : Squares over Squares
Exp. #13 : Analog Clock Implementation #1
Exp. #14 : Sound Visualizer Implementation #1
Exp. #15 : Vector Field : 2D
Exp. #16 : Animation inspired from Crop Circle pattern
Exp. #17 : Gaussian Distribution of Points
Exp. #18 : Data Viz #1 : Pi upto 500 decimal places.
Exp. #19 : Spiral Pattern Implementation #1
Exp. #20 : Slightly Useful Typewriter
Exp. #21 : Fractal Implementation #3 : Circles
Exp. #22 : Dussehra 2017 - 2D Fireworks.
Exp. #23 : Spooky Eyes
Exp. #24 : Mouse Torch
Exp. #25 : 3D Oscillations #1 - Cubes
Exp. #26 : Packing : Circles
Exp. #27 : Illusion with Circles #2
Exp. #28 : 10 PRINT Pattern Implementation #1
Exp. #29 : Fractal Implementation #4 : Arcs
Exp. #30 : Deception with Colours #1
Exp. #31 : Yin Yang
Exp. #32 : 2 dimensional iterative animation #1 : Maze
Exp. #33 : Sound Visualizer #2 : Bars
Exp. #34 : Pixel Data Manipulation #1 : Dance!
Exp. #35 : Cube Layers.
Exp. #36 : Fractal Implementation #5 : Cubes
Exp. #37 : 2D Shape Customizer
Exp. #38 : Fractal Implementation #6 : Suits Animation
Exp. #39 : Algorithmic Botany : Tree v1
Exp. #40 : Sound Propagation : Compression & Rarefaction Animation
Exp. #41 : Happy Diwali!
Exp. #42 : Mirrored Drawing Pad
Exp. #43 : The Matrix Terminal
Exp. #44 : Illusion with Circles #3
Exp. #45 : Illusion with Squares #2
Exp. #46 : Illusion with Circles #4
Exp. #47 : Illusion with Polygons : Hexagon #1
Exp. #48 : Illusion with Circles #5
Exp. #49 : Rotating Rectangular Brush
Exp. #50 : ½ Century
Exp. #51 : Abstract Geometrical Art #1
Exp. #52 : Abstract Geometrical Art #2
Exp. #53 : Noisy Plane
Exp. #54 : EMT - Ring & Point Charge System
Exp. #55 : Abstract Geometrical Art #3
Exp. #56 : Deception with Colours #2
Exp. #57 : Neural Network Viz #1
Exp. #58 : Neural Network Viz #2
Exp. #59 : Seizure Inducing Illusion
Exp. #60 : Fractal #7 : 3D Vicsek
Exp. #61 : SHM : Spring, Bob & Hinge System
Exp. #62 : Mathematical Flowerinator
Exp. #63 : Oscillating Sliders
Exp. #64 : Algorithmic Botany : Phyllotaxis
Exp. #65 : 3D Sound Visualizer
Exp. #66 : 2 Stars & 200 Particles
Exp. #67 : Dipole & Magnetic Particles
Exp. #68 : Young’s Double Slit Experiment
Exp. #69 : Abstract Geometrical Art #4
Exp. #70 : Fractal Spirograph v1
Exp. #71 : Algorithmic Botany : Trees #2
Exp. #72 : ‘Trend’ Line Calculator
Exp. #73 : Sound Viz - Mickey Mouse - Hot Dog
Exp. #74 : Menger Sponge Fractal
Exp. #75 : Schematic Diagram of a DC Machine
Exp. #76 : Stars and Particles : v2
Exp. #77 : 2D Rain Simulation
Exp. #78 : Metaballs / Isosurfaces in 2D canvas
Exp. #79 : Iterative Sketching #1 : Sun & Moon
Exp. #80 : Mountain Landscape
Exp. #81 : Abstract Geometrical Art #5
Exp. #82 : Geometry Viz : Area of Δ
Exp. #83 : Particle Beanstalk
Exp. #84 : A Globe
Exp. #85 : Mouse Seekers #1
Exp. #86 : Metaballs / Isosurfaces v2
Exp. #87 : Crazy Cells
Exp. #88 : Pixel Data Manipulation #2 : Ascii Art
Exp. #89 : Abstract Geometrical Art #6 : Hex Nuts
Exp. #90 : Iterative Sketching #2 : Furry Smoke
Exp. #91 : Iterative Sketching #3 : Stain
Exp. #92 : Arbitrary Sketch #1
Exp. #93 : Arbitrary Sketch #2
Exp. #94 : Squiggly Waves of Sun
Exp. #95 : Mouse Seekers #2
Exp. #96 : Iterative Sketching #4 : Fire Pit
Exp. #97 : Shape Morphing #1 : ▲ to ⬤
Exp. #98 : Shape Morphing : + Controls
Exp. #99 : Artificial Life : Flocking Agents #1
Exp. #100 : Artificial Life : Path Following Bots
Exp. #101 : Arbitrary Sketch #3 : Hypnotic Iris
Exp. #102: Collision Detection : Particles
Exp. #103 : Koch Snowflake
Exp. #104 : Extracting Simpler Oscillations
Exp. #105 : Psychedelic Noisy Vectors
Exp. #106 : JS Reserved Keywords (2017)
Exp. #107 : Maze Generation #1 : RB
Exp. #108 : Conway’s Game of Life v1
Exp. #109 : Metaballs / Isosurfaces v3
Exp. #110 : Abstract Geometrical Art #7 (3D)
Exp. #111 : Iterative Sketching #5 : Solar Flare
Exp. #112 : DNA : Double Helix (3D)
Exp. #113 : Abstract Geometrical Art #8 (3D)
Exp. #114 : Artificial Life : Flocking Agents #2
Exp. #115 : Artificial Life : Flocking Agents #3
Exp. #116 : Abstract Geometrical Art #9 (3D)
Exp. #117 : Abstract Geometrical Art #10 (3D)
Exp. #118 : Abstract Geometrical Art #11 (3D)
Exp. #119 : Electromagnetic Wave Propagation
Exp. #120 : Abstract Geometrical Art #12 (3D)
Exp. #121 : Abstract Geometrical Art #13 (3D)
Exp. #122 : Abstract Geometrical Art #14 (3D)
Exp. #123 : Abstract Geometrical Art #15 (3D)
Exp. #124 : Color Spray!
Exp. #125 : Color Explosion!
Exp. #126 : Abstract Geometrical Art #16
Exp. #127 : Immortal Snake Adventures
Exp. #128 : Algorithm Visualizaiton : TSP - I
Exp. #129 : Ripple
Exp. #130 : Rainbow Rain
Exp. #131 : 7 Segment Display
Exp. #132 : Stack Overflow 3D
Exp. #133 : Valentine’s Day
Exp. #134 : Spheres on a Sphere
Exp. #135 : Pixel Tunnel
Exp. #136 : Algoviz : A* Search
Exp. #137 : Jelly Fish Prototype
Exp. #138 : Celebratory Explosions
Exp. #139 : Life Spreading Brush
Exp. #140 : Artificial Life : Cockroaches
Exp. #141 : Evolutionary Rockets
Exp. #142 : Connected Spring Mass System
Exp. #143 : Video Pixel Manipulation
Exp. #144 : Football Field
Exp. #145 : Video Pixel Manipulation 3D
Exp. #146 : Unstable Colorful Spirits
Exp. #147 : Squiggly Life*
Exp. #148 : 3D Oscillations : Spheres on Sphere
Exp. #149 : 3D Oscillations : Black Hole Effect
Exp. #150 : Wind
Exp. #151 : 3D Oscillations: Rectangular Sheets
Exp. #152 : Poisson Disc Sampling
Exp. #153 : Radially Shrinking Superellipses
Exp. #154 : Animated Cochleiod
Exp. #155 : Hexaskelion
Exp. #160 : Liquid Simulation (Interactive)