How to stand out as a data scientist in 2021

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Data science has been one of the most attractive fields to work on over the past decade and to change. The neighborhood is evolving at a breakneck pace. What a data scientist was doing a few years ago has already been or is about to be automated – the goal post is moved with every turn.

“Automation is no longer just a problem for those working in manufacturing. Physical labor has been replaced by robots; mental work will be replaced by AI and software.

Andrew Yang

The data science job market is very competitive. The survival of a data scientist rests on Toffler’s mantra: learn, unlearn and relearn.

Here, we take a look at what it takes to stand out as a data scientist in 2021.

Specialize

The jack-of-all-trades no longer cuts it off. While data science has many applications, people will pay more if you are an expert in one area. For example, your value as a data scientist will be worth its weight in gold if you are exceptional at visualizing data in a particular language rather than a gamer of odds and ends. The main technical skills in demand in 2021 are data wrangling, machine learning, data visualization, analysis tools, etc.

“” German for all trades, master of nothing “is an idiom which is becoming omnipresent in development. The idea is that a developer who touches everything can’t be amazing at anything.

Parker software Blog

To explore

As a data scientist, it is imperative to know your fundamentals cold. It would help if you spent enough time with your data to extract actionable insights. A data scientist needs to hone her skills by exploring, plotting, and visualizing data as much as possible.

Deploy

Most data scientists or aspiring data scientists who do statistics learn to code or take some machine learning or statistics courses. However, it’s one thing to code small models on training platforms and another to build a robust machine learning project that can be deployed in the real world. Typically, data scientists need to learn the basics of software engineering and real-world machine learning tools.

“No machine learning model is valuable unless it is deployed in production. ”

Luigi Patruno

Know your math

One of the most important things for a data scientist in 2021 is to keep abreast of the latest developments in mathematics. This is crucial if you want to create state-of-the-art machine learning systems. Most frameworks will allow you to easily build models or networks without having a deep knowledge of math. But, if you want to stand out as a data scientist, a solid foundation in calculus, linear algebra, and statistics is essential.

“I’ve always been a strong advocate of self-study, and I think that’s where you get maximum value”

Dipanjan Sarkar

Build a portfolio

If you are looking for a job, getting an interview can be very difficult. There is so much competition and the options for a recruiter for data science roles are endless. What can help you stand out is a strong portfolio with various data science projects. A good Github portfolio on your CV can help you get noticed. Target at least one hands-on open source project each month to keep your portfolio up to date.

Sometimes even getting to the interview can be difficult. This ties in well with the example given in the last tip. There is nothing more powerful about your application than the proof that you have already done this kind of work ”

Dr Adam Sroka

Take part in ML hackathons on MachineHack

Publish

A data scientist must keep abreast of the latest research in the field. Having said that, original research is always a huge plus. The best way to do this is to publish research articles in well-known journals. The proof is in the pudding. Ideally, you should aim to publish at least one research article in six months.

Find out how you can write for Analytics India Magazine.

There are different Slack channels or Meetups to stay up to date with the latest events in the field. Joining a community will help you stay connected and give you visibility.

You can join the AIM Telegram community.

Sean N. Ayres