Rule to Transition from Data Scientist to Full-Stack Data Scientist

Rule to Transition from Data Scientist to Full-Stack Data Scientist

Organizations and job markets are continuously tagging ‘Data science’ and ‘AI’ to everything pertaining to data, which can lead to misconceptions about the skill sets required to be a successful data scientist. Let’s add the word ‘Full-stack’ to it to make it a little more vague, and then try to define it.

There are blogs geared at complete beginners, posts aimed at senior software engineers, and those
designed to help junior data scientists develop their abilities, I believe, a lot of data science job advice follows a similar logic. Many aspiring data scientists find it challenging to decide where to devote their attention when they try to break into the industry because of all of the noise. And, while there is no one-size-fits-all solution for everyone, I’ve discovered that the same advise is given to about three different groups of people.

Category 1: Complete Beginners

If you’re just getting started in data science, bear in mind that the area is rapidly growing, so any advise I give you now will most probably be outdated by the time you’re ready to start working. What got people recruited in 2017 doesn’t work today, and the gap between today’s data science hiring standards and those that will apply in one or two years will most likely be substantially greater.

● Keep an open mind first and foremost. If you’re a complete newbie, you don’t know what data science is by definition, so it’s perfectly feasible that it’s not the job for you after all. Make contact with several data scientists on LinkedIn and offer to buy them coffee and have a conversation.

● That’s fantastic if you decide to move forward! First and foremost, you’ll need to learn Python. Take a MOOC and start working on a basic project right away.

● If you’re actually starting from scratch, pursuing a full-time data science employment may not be the ideal option. Instead, go for the low-hanging fruit: data visualisation and data analytics positions are in high demand and are easier to break into.

How to Brand Yourself

If you’re ready to start applying for jobs, you might be shocked to hear that developing a personal brand is very vital in data science. You could be concerned that you won’t be able to brand yourself since you lack professional experience or a graduate degree in computer science.

Category 2: Software Engineers

Software engineers make up about 20% of the wannabe data scientists I meet. On the one hand,
experience releasing code to production and working with development teams can be extremely
beneficial. On the other hand, demand for full-stack developers is so great that some businesses find up nudging software workers in that way, even if the position they were hired for on paper required “data science.” As a result, you’ll want to avoid being labelled a software engineer instead of a data scientist.

Some other thoughts:

● If you haven’t already, think about transitioning your present position to a more backend/database-focused role. Getting to know data pipelines is a fantastic place to start, and it can help you develop your core data manipulation skills.

● Data Science and Machine learning to engineering is perhaps the most closely connected data science-related career, making it a more straightforward transition. Focus on roles that stress installing models or integrating them into existing programmes, as they will best utilise your current skillset.

● To impress companies, you’ll almost certainly need to design machine learning or data science projects. Make the most of your software engineering talents by incorporating them into apps that you can show recruiters and technical leads.

● Keep in mind that you will almost probably lose money during your changeover. When it comes to data science, even senior software engineers must typically transfer to junior roles. How to Brand Yourself Utilizing your software development knowledge is one of the simplest methods to establish your brand. You already know how to produce clean, well-documented code and work with others, which is a skill that most junior-level applicants lack.

Category 3: New CS, Math, or Physics Grads

You probably have an excellent foundation in statistics and math if you’re a fresh undergraduate, Master’s, or Ph.D. STEM graduate. However, you’ve probably never applied for a job in technology before and have no idea how to prepare for an interview. Furthermore, presuming you’ve been programming throughout your education, you’re unlikely to be able to write clean, well-organized code.

A few things to keep in mind:

● No, the R you learnt in college will not enough. No, if you’re a physicist hoping to gain a job in industry with your MATLAB or Mathematica skills, those won’t make it either. All you have to do is learn Python.

● In Python, you’ll learn how to do test-driven development. Learn how to utilise docstrings in
your code. Learn how to break down your code into modules. Learn how to use Jupyter notebooks if you haven’t before.

● Deep learning may be an excellent direction to consider if you work in a particularly math-
oriented field. However, you may find it easier to begin with a more traditional “scikit-learn” data science role and then transition to deep learning later.

How to Brand Yourself

Your greatest tactic, especially if you’re a math or physics major, is to portray oneself as someone with extensive theoretical knowledge.


Whether you’re a software engineer, a recent graduate, or a complete novice, a vital question to ask oneself is what job paths are closest to you in parameter space. If you need to get your foot in the door, a stint as a data analyst or data visualisation specialist can sometimes be the greatest approach to set you on the appropriate long-term path.

Also read : Different Kinds of Research and Research Skills

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About the Author: Inaayat Chaudhry

Inaayat Chaudhry is a staff writer and digital marketing analyst at AGM Search India, Specializing in Digital Marketing and Tech.

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