Raghu Mohan

Who is a Full Stack Developer?

A good full stack developer is like a celebrity who can do it all—act, sing, DJ, host, direct, and produce. While they may not win an Oscar or Grammy, they bring versatility and breadth of experience to the table.

They are capable of building complete applications—web, mobile, or desktop. They understand both front-end and back-end development, and are familiar with servers, databases, APIs, MVC architecture, and hosting environments. (Top skills a full stack developer should have)

Full stack developers are in high demand with thousands of openings on job platforms. However, they may not always be the right choice for every scenario.

When to Hire a Full Stack Developer

1. When You Need an MVP

   For startups or lean operations aiming to validate ideas with a minimum viable product, full stack developers are ideal. They can convert concepts into functional prototypes efficiently.

2. When You Need Product Managers

   With their technical and business understanding, full stack developers often make great product managers who can balance both sides effectively.

3. When Cost Is a Constraint

   If hiring specialists for every layer isn't viable, a full stack developer can provide excellent value. One skilled developer may cost less than hiring three specialists. (Recruit on a shoestring budget)

4. When You Need a CTO/Co-Founder

   Full stack developers make excellent co-founders or CTOs, especially for those seeking a partner to bring a technical idea to life. (More on CTO as a service)

When Not to Hire a Full Stack Developer

Do not hire a full stack developer if there is no clear value-add. At a large scale, specialized teams for each layer—data, infrastructure, front-end, etc.—are more effective than one generalist.

How to Hire a Full Stack Developer

Qualities to Look For

• Passion for continuous learning
• Broad understanding of various technologies
• Problem-solving mindset, even if not hands-on
• Stays updated with tech trends
• Understands the business vision and user needs

Technical Skills

For web app development, a full stack developer should know:

• HTML, CSS, JavaScript
• Back-end programming languages
• Databases
• Version control (e.g., Git)
• Deployment and hosting
• Third-party APIs/services

More on required skills: Top skills for full stack developers

Resume Insights

Don't rely solely on resumes for technical evaluation. Look for:

• Open-source contributions
• Varied tech exposure
• Real-world project experience

Alternative sourcing platforms like GitHub are often more insightful.

Technical Assessment

A real-life project is a better assessment than algorithmic tests. Use practical business problems to evaluate full stack skills effectively.

Interview Focus Areas

• Ability to deal with uncertainty
• Curiosity and eagerness to learn

Present unfamiliar problems and observe how candidates approach solutions—not just the outcome.

Hiring Tips Summary

• Look for inherent developer traits
• Make technical evaluations mandatory
• Use appropriate methods to test technical knowledge

Use tools like HackerEarth Assessments to streamline the hiring process.

Popular Posts Like This:

1. How to Hire a Data Scientist
2. The Complete Guide to Hiring a Data Scientist
3. 8 Ways to Hire a Developer

Subscribe to the HackerEarth blog and receive free monthly articles on tech recruiting and development straight to your inbox.

President Trump signed two bills in February 2017, aiming to boost the number of women in STEM. He said, “Currently, only one in every four women who gets a STEM degree is working in a STEM job, which is not fair.” (Science, Technology, Engineering, and Mathematics when taught and applied in an interdisciplinary approach are collectively referred to as STEM.)

United Nations has designated February 11 as the International Day of Women and Girls in Science “to achieve full and equal access to and participation in science for women and girls, and further achieve gender equality and the empowerment of women and girls.” All this is part of a widespread effort, including societal shifts, political activism, governmental initiatives, and other promising steps by private groups, to get more females in these fields that have been male-dominated for more years than one can count. Read this post to look at the world championing the cause of women in STEM.

By 2018, 71% of the jobs will require STEM skills. In 2013, only 26% of the computing professionals and 12% of the engineers were women. In every region of the world, women are underrepresented in R&D, with an average of just 28%, says UNESCO. European countries are struggling with gender equality and limited access to employment issues. The situation is pretty much the same or worse in developing nations. If you want to look at some surprising statistics about women in tech, go here.

Socioeconomic obstacles to overcome

Many women have to fight stereotype threats; often, the battle is within. Barring innate competence or abilities in math, culture seems to have a lot to answer for when you look at the mathematical “performance gap.”

Here’s what the Organisation for Economic Cooperation and Development (OECD) said based on its international study on gender equality in schools: “What emerges from these analyses is particularly worrying. Even many high-achieving girls have low levels of confidence in their ability to solve science and mathematics problems and express high levels of anxiety towards mathematics.”

Girls seem to be faring poorly frequently because of low expectations of teachers and family, who seem to be perpetuating the girls aren’t good at math myth. Vulnerable, these girls report low self-confidence. Teachers, perhaps unwittingly, underrate the math skills of their girl students.

The OECD added: “This gender difference in the ability to think like a scientist may be related to students’ self-confidence. When students are more self-confident, they give themselves the freedom to fail, to engage in the trial-and-error processes that are fundamental to acquiring knowledge in mathematics and science.”

Eradicating centuries of cultural conditioning isn’t easy. But we have to start sometime.

No point in blaming men alone

Skewed perceptions affect hiring decisions. Research shows that employers are twice as likely to hire a man for a math job; it didn’t matter whether the person doing the hiring was man or woman. Women are underestimating themselves, and apparently, they are letting the unconscious bias pull down other women as well.

Unfortunately, some girls also find math boring.

Girls believing that they are ‘simply not good at math’ and giving up easily are not helping. Telling them it is OK to fail but letting them know persistence and confidence are not to be shrugged off go a long way toward the cause.

UNESCO’s research shows that females may be more anxious about STEM subjects. Explaining the gender gap in math test scores: The role of competition gives you another perspective about gender difference in mathematical skills.

Not a good situation this…

Media and mothers have a part to play

Have you noticed how many TV shows and movies portray smart women as geeky and awkward and the beautiful ones as slow and ‘stupid’? Girls often choose not to be the ‘odd one’ out and downplay their talent. They find reasons to not try.

Even toys—girls get dolls, boys get meccano. No point in wondering why we don’t have enough engineers, you know. Some mothers keep telling their children how they should go to the dads to solve a math problem and empathizing with their daughters who aren’t doing well at math. This needs to stop. You are unknowingly reinforcing a negative stereotype. Stop setting lower expectations and start connecting with them in positive ways.

Myth or reality

The myth that women are born disadvantaged as far as math skills are concerned have been debunked by enough research studies. Equal aptitude skills, that’s what data shows. ..Girls are not destined to do badly in math.

Look at the OECD Programme for International Student Assessment (PISA) data gleaned from 15-year-olds in 65 countries.

Source: http://www.noceilings.org/stem/

Seems like it is all about perception, doesn’t it?

Time's a-wastin'

“We have been discussing the issues of STEM careers and gender imbalance for too long! It is now time to take action, as other nations have done. A coalition of stakeholders from government, industry and education needs to come together as a matter of urgency to decide on a course of action,” said Professor Brian MacCraith, DCU President and Chair of National Review of STEM Education 2015.

That’s exactly what companies such as HackerEarth are trying to do. As part of its initiative to empower women in tech and encourage women developers world over, the company is conducting an International Women’s Hackathon 2017 on Women’s day. For women readers who would like to give it a shot, go here.

Have you ever understood the process of learning? How you learn how to perform a particular task? How do you learn to play a musical instrument? How do you learn social convention? How do you learn to lie? Apart from humans, plants and animals possess the ability to learn. And in recent times, so have machines.

Today machine learning is responsible for everything from Siri, Google Now, the recommendation engine on YouTube and Netflix, to even the driverless cars. Surely, machine learning is something that every computer scientist will encounter sooner or later, and it is important to learn this well.

History of Machine Learning

Machine learning was the byproduct of the early endeavours to develop artificial intelligence. The aim was to make a machine learn via data. But the use of this approach often resulted in reinventing already existing statistical models. This, coupled with the increase in knowledge and logical based approach to AI put machine learning out of favour among the AI community. Machine learning soon became a sub-sect of statistics and data mining.

But, over time, Machine Learning has become a separate field of its own. Instead of striving to achieve artificial intelligence, the main aim of machine learning has become more towards tackling solvable problems. It borrows techniques from statistics and probability to focus on predictions derived from data.

Machine Learning and AI

Ever since the rebirth of machine learning as its own field, there has been many a debate on the distinction between machine learning and AI.

There is one group that believes that machine learning is the only kind of AI there is. As machine learning enables a machine to learn based on external stimuli, it is in essence mapping the mind, which makes it the only kind of AI there is.

There are those who also believe that while machine learning is AI, AI is more than just machine learning. AI includes concepts like symbolic logic, evolutionary algorithms and Bayesian statistics and many other concepts that don’t fall under the purview of machine learning.

The important thing to note is the goals that each one of them try to achieve. Apart from machine learning, AI tries to achieve a broad range of goals like Reasoning, Knowledge representation, Automated planning and scheduling, Natural language processing, Computer vision, Robotics and General intelligence. Machine learning on the other hand focuses on solving tangible, domain specific problems through data and self learning algorithms.

So what are the skills you need to learn to get started with machine learning? Read on -

Seeing Is Believing

R2D3 has one of the best visual representations of what machine learning is. It takes the example of room sizes and elevations of homes in different cities in the US. This visual representation is a walkthrough of the techniques used to build an algorithm that will be able to predict the location of the house based on the dimensions of the rooms. Check out R2D3.

Another great resource is this blog on the Scikit learn site. In fact, at some point you will use Scikit learn, which is an open source machine learning library for the Python programming language.

Kaggle is one of the sites known for machine learning contests. This guide is a great starting point for anyone with a little bit of programming skills.

The Tongue

While most programmers prefer Python for its all-round capabilities and its data exploration prowess. However, the kind of programming language that you need to use depends on the machine learning application. This answer on Quora by a former Googler beautifully elucidated the process behind choosing a language while building your machine learning algorithm. Apart from Python, languages like C++, R and Java are popular among machine learning enthusiasts.

There are many libraries in Python for machine learning. Pybrain is one of them. As mentioned before, Scikit learn is another popular machine learning library in Python.

Key Skills

The language is only one of the many skills that you need to be adept at machine learning. You will need sound understanding of probability and statistics, applied mathematics and algorithms, distributed computing, big data and even signal processing techniques. Joseph Misiti, a data scientist at Square, explains the intricacies of each of these requisites.

This blog post on Udacity by a data scientist at Airbnb is a great post on the skills that a data scientist in today’s age is expected to have.

CMU has a free and open course on statistics for machine learning, with video lectures, assignments and solutions as well.

The Attitude

And lastly, and perhaps the most important trait that someone pursuing machine learning needs, is the attitude of a data scientist. This article from the Zipfian Academy elucidates the mindset that a good data scientist needs and this mindset goes a long way in being good with machine learning as well.

While you’re at it, you should also check out the machine learning track at IndiaHacks. It’s a fantastic opportunity to get your hands dirty with a real time machine learning problem and put yourself to test with some of the best talents in the machine learning space.

There are about 20 major stock exchanges around the world, with each country having many more regulated stock exchanges. A large stock exchange like NASDAQ, does about 10 million trades, with over 1-2 billion shares traded every day.

Movies like the Wolf of Wall Street have popularised the image of a stockbroker and their glamorous high flying life. And if your image of what a stock trading unit looks like, is something like the image below, then we don’t blame you -

This image is very disconnected from reality. Almost 90% of all short term trade, and about 50-70% of all trade, is done by stock trading algorithms. These are machine learning algorithms that buy and sell in the stock market, at a phenomenally fast pace.

There are many kinds of stock trading algorithms - from those which will execute simple buy and trade functions (at phenomenally high speeds, mind you), all the way to analysing the news to forecast which stocks to buy and sell. Here’s a really nice explanation of all the different kinds of stock trading algorithms - http://hck.re/erIYgs.

In fact, there are some algorithms that exploit the behavior of an opponent's algorithms for a profit. Mike Beller, the CTO of Tradeworx explained a classic example of what an exploitative algorithm can do. In 2012, an algorithm started to rapidly buy the stocks of food company, Kraft foods. This artificially increased the value of each of these stocks. It is said that the algorithm spent about 200,000 USD in buying the stocks and then sold it for a whopping 900,000 USD. That’s over half a million dollars in profit.

Of course, this was corrected and all trades pertaining to this was cancelled and dubbed as a technical error. However these algorithms are phenomenally powerful, and to a large extent, humans are at its mercy. How, you ask? Read on -

The real need for speed..

It is said that a simple buy and sell algorithm can execute up to 1000 trades a second. Compare this to 11-12 seconds that a human takes to execute a single trade. So, how did we get here?

It is imperative, that the first one with information, is usually going to win. It’s the same logic behind kings using pigeons to send information, so as to beat the enemy king’s man on horseback. This same is more pertinent in stock trading.

The simple mantra of getting rich in the stock market, whether it's through traditional investments or pocket options trading is buying low and selling high. So the first to get information about a rising stock has the advantage of making more money than someone who gets the information at a later time. This simple need, set off a mini arm’s race in the stock trading market.

.. Is when the speed of light isn’t fast enough

As electric pulses, the speed of the data is restricted by the speed of the electron, which is about 2,200 km/second. With fiber optic cables, the speed of data transmission is restricted only by the speed of light, which is about 300,000 km/second. These are mind boggling numbers, but in the world of algorithmic stock trading, even microseconds in delay is a missed opportunity.

Let me explain - someone sitting in New Jersey will receive information faster from New York, than someone sitting in California. The distance needed to cover is lesser, so you’ll get your information much faster. Of course, this is the speed of light that we’re talking about, so the difference is really in microseconds.

But that’s more than enough for an algorithm to execute a trade. It is estimated that an algorithm can execute a trade in about 10 microseconds. This became an issue in the USA, as traders began buying real estate closer to the NYSE and NASDAQ so as to counter this problem.

The solution that NYSE came up with, is to provide server rooms for companies, where a company’s server will be placed in a room right next to the NYSE server rooms, and the cable length from the NYSE servers to a company’s server will be exactly the same, for everyone who buys space in NYSE.

A fair solution - only it didn’t stop the arms race.

We’re more connected than ever

Back in the day, the US had 2 stock markets, namely NYSE and NASDAQ. As of 2 years ago, the US has 13 regulated exchanges and over 50 dark pools. And not all of them provide the on premises solution that NYSE provided. But that’s not even the problem.

There are different kinds of stock markets. There are markets that sell company equity and there are those which sell commodities, like gold, copper, wool, oil etc. And these markets are connected.

Take the case of oil - if the value of oil goes up in the commodity market, a petroleum company’s stock also increases. And these markets are not even in the same place. So, we’re back to the same need for speed.

In the US, there is a commodity market in Texas and the NYSE and NASDAQ is in New York. Massive amounts of money has been spent in creating a straight communication line between the two markets - blowing up mountains on the way to lay wires is just one of the many outrageous things that were done for this. All of this effort has achieved a maximum efficiency of 13 microseconds for an up and down communication. This is still slow, as a computer still has to wait an enormously long 3 microseconds before it can execute a buy/sell function.

When will this end?

Not anytime soon. It’s been found that the speed of light is faster in air, as opposed to a fiber optic cable, which has further reduced the time to 8 microseconds for communication between Texas and New York. Who knows what we’ll find tomorrow?

And given that things are happening at such breakneck speeds, it’s very tough to analyse and find the reason behind many stock market crashes. Listen to the last 10 minutes of this Podcast for one fascinating event. A circuit breaker tripped and the NYSE lost power for 5 seconds. The market plummeted almost a 1000 points. Funnily enough, it came straight back up as soon as the power came back. Worryingly, no one really knows what caused it, even to this day.

As with any other profession, every time a computer replaces a human, 2 things happen:

1. The execution of the task becomes more efficient - The arms race is only going to result in better efficiency and performance in stock trading.
2. The execution of the task also becomes cheaper - 10 years ago, it costed 100 USD to trade 1000 shares. Today, 10 USD for the same.

But in this case, one thing remains. Machine learning has given bots a brain of their own and because of the speed at which things happen, incidents like the unexplained stock market crashes leaves us with the haunting question - how long till we lose control?

There are 3 tracks at IndiaHacks that will test your expertise at a skill that has resulted in the bots of wall street -

1) Fintech

2) AI challenge

3) Machine learning

Give them a shot at IndiaHacks!