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Intro to Quality Engineering - NEW? START HERE
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:
… 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)
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.
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.
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:
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:
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.
[For Hire] Frontend Developer - I will create Marketing and Web & Mobile Applications in Vue, React, React Native.
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 :- Artificial Life - Bird Flocking Algorithm Illusion with Circles 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)
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 :- Artificial Life: Bird Flocking Algorithm Mathematical Operations on Two Waves Illusion with Circles 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) That's all folks! These are my public projects from 3 years ago, I've built many more things in many more languages and frameworks since then but mostly for startups for which I had to sign NDAs, therefore I can't show them off here.
selenium automation engineer salary in india video
16 Selenium Automation Test Engineer Salaries in Chennai, India provided anonymously by employees. What salary does a Selenium Automation Test Engineer earn in Chennai? The mean salary of a Senior Quality Assurance (QA) / Test Automation Engineer in India, having skills of Selenium Automated Test Tool is INR 889,304. So, this can be regarded as the national average. So far, we have discussed average Selenium tester salaries in India for freshers and experienced based on factors such as experience, skills, and employers. The community relies on everyone sharing – Add Anonymous Salary. Selenium Automation Test Engineer. ... (India) Selenium Automation Test Engineer salaries - 1 salaries reported ₹471,951 / yr. Don't Miss Out On a Job You Love. Upload a resume to easily apply to jobs from anywhere. Salary: upto 10 LPA . Sr. Python Selenium Automation Engineer. Experience: 3 to 7 years. Salary: upto 15 LPA . Python Selenium Automation Lead. Experience : 7 to 13 years. Salary: upto 23 LPA . Location : Pune (Baner) Skills Required: · Hands on experience with Python-Selenium or Cypress. · Expert at test automation, creating test plans, test ... Average Infosys Selenium Automation Engineer salary in India is 5.2 Lakhs per year as shared by 4 employees. Know how much do Infosys Selenium Automation Engineer employees earn by experience, location and roles. 0L1L2L3L4L5L. Years of Experience. Avg. Salary (in Lakhs) Selenium Automation salary with less than 1 year of experience to 3 years ranges from ₹ 2.5 Lakh to ₹ 19.0 Lakh with an average annual salary of 7.2 Lakhs based on 118 salaries. The average salary range of Selenium Test Engineers in India is between 3,50,000 and 5,00,000. The Senior automation engineers earn a salary of >7,00,000. In US salary is between $40,000 and $75,000. According to payscale.com, the average salary of an Automation Engineer in India is around 4.5lakhs per annum at an initial stage. Selenium Career Opportunities: Selenium Jobs in 2021. In this blog on ‘Selenium Career Opportunities,’ we will talk about various Selenium career opportunities, what led Selenium to become so popular in the automation testing domain, Selenium job salary trends in India and in the US, the scope of Selenium, how the demand for Selenium Testers would increase in 2021, and much more. So the common selenium wage in India is around 1,950,000 each year. The wage vary of selenium professionals falls between 1,000,000 each year to 4,200,000 each year. This variation in wage depends upon varied factors, akin to job location, expertise, education, employer, certifications, and others. Also Read :
