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Blog: Now that you want to be a Data Scientist, What Next?

Greetings, Tech Fanatics! Did you know there are more than 97,000 analytics and data science job positions (6% of overall open jobs across the world) available in India? Well, there are! By 2026, the world will need 11.5 million data scientists. The demand is only growing with almost 45% increase in the job openings, compared to last year. Thanks to the exploding demand for data professionals but there are not enough candidates to fill them. Let’s face it, becoming a data scientist has some major perks — it’s fun and rewarding.

Mr. Minton, a 26-year-old math major, who took a three-month course in computer programming and data analysis. As a waiter, he made $20,000 a year. His starting salary last year as a data scientist at a web start-up was more than $100,000. Do the growing demand and unbeaten six-figure salaries for a continually evolving field like data science entice you to become one?

If you answered a big YES to this question then this article is a must-read for you to get on board the data science bandwagon.

Now that you want to become a data scientist, I’m going to show you the next steps to become one. This article will give you a solid plan to get started with data science and land your first lucrative gig as a data scientist.

If you’re an experienced data scientist already, save yourself the time to do something else. Otherwise, read on to become one.

Becoming a data scientist is a progressive process that builds up your data science skills day after day and year over year. In this article, we do not promise you to give any magic mantra to becoming a data scientist, but you will get a quick outline on how to become a data scientist and the skills you need to become one.

Five Steps to Becoming a Data Scientist — The Data Science Career Path

  • Understand the Data Science Job Role

First things First — What does a data scientist do?

Before we start looking at how to become a data scientist, I need to ensure that you have an in-depth understanding of the data scientist job role and what does a data scientist do. In simple terms, the ever-mysterious data scientist job role involves data analysis to produce actionable insights for business decision making. A data scientist job involves multiple tasks such as data pre-processing, data sampling, model estimation, model validation, model deployment, backtesting and a lot more.

A data scientist analyses the organization’s treasure troves of data to identify trends and uses his/her business acumen to recommend what and how business problems can be tackled using data science. For example, in a news company, a data scientist can help discover compelling news stories nobody else has, help increase the subscriber revenue, or help with election forecasting. At an advertising company, a data scientist can help clients with customer segmentation based on the location, demographics, and interests so they can target the ideal audience for their products through relevant ads.

According to Quora User Thia Kai Xin, Head of Data (Tech In Asia), Co-Founder of DataScience SG, here’s what a typical day in the life of a data scientist looks like –

To understand a data scientist job role better, do this –

Go to Indeed, LinkedIn, or Glassdoor — Look for terms like “data scientist”, “junior data scientist”, “senior data scientist”, and “applied scientist”. Set a goal for yourself to read at least 8 to 10 data scientist job description. Having read them, make a note of what’s universal and common across all the 10 jobs and what is unique to any given job description. This will give an in-depth understanding of the data scientist job role, and what different types of data scientists work on each day.

Now that we have answered the basic question on “What does a data scientist do?”, let’s move onto understand what skills you need to become a data scientist.

  • Build the right Data Science Skills Tool Belt

Technical Skills

i) Statistics and Probability

A good data scientist must have strong foundations of statistics, algorithms, probability, and mathematics. In case you do not have, we suggest you begin with learning the basics of statistics and math with a major focus on set theory, graphs, linear algebra, functions, and probability. There are lots of free and paid courses on Statistics and Probability on Udemy, EdX, Coursera, and more.

Key Concepts that you need to learn include — Maximum likelihood, conditional probability, probability distributions, regression, hypothesis testing, statistical significance, and priors and posteriors. These terms might seem mumbo jumbo to you for now but not to worry once you start learning they will make complete sense.

We suggest reading Think Like a Statistician (Think Stats) and Think Like a Bayesian(Think Bayes) to learn the underlying concepts in a fun and intuitive way. If you’ re hungry for more take some foundational courses on Probability and Statistics.

Here are our best picks to learn the basics of Statistics and Probability for Data Science –

Statistics Using R by the University of Texas at Austin (edX)

Inferential Statistics

ii) Programming Skills

Programming is an integral part of a data scientist job role. Programming languages like Python or R help data scientists flexibly and efficiently clean, extract, analyze, and visualize data. According to a study conducted by 365 Data Science, 53% of data scientists either use Python and/or R programming language. Python and R programming language are the bread-and-butter for data scientists. An aspiring data scientist should be familiar with at least 3 out of 5 of these data science tools– Python, R, Hadoop, SQL, and Tableau.

iii) Machine Learning

As a data scientist, you will be working with treasure troves of data that will require you to be familiar with machine learning models like — Random Forest, Decision Trees, K-nearest neighbors, Ensemble methods, and more. A lot of these machine learning algorithms can be implemented using Python or R programming language, hence one need not have expertise on the working of these algorithms. Gain some broad knowledge and understanding of the popular machine learning algorithms so that you are capable of deciding when to use various machine learning techniques.

iv) Data Visualization

“A picture is worth a thousand words.” Pictures make it easy to communicate the insights to stakeholders than either words or numbers. Visualizations help data scientists convey a compelling story with data to keep the audience engaged. As a data scientist, you will be required to present data and insights in a visually appealing way through eye-catching, high-quality graphs and charts in a clear and concise way. Make yourself familiar with the principles of data visualization and master a couple of popular data visualization tools like Tableau, Kibana, Google Charts, and more.

Non-Technical Skills

i) Business Acumen

Technical skills will help you get started with your career as a data scientist but to create a mark as a great enterprise data scientist you will have to develop the right business acumen to foster growth. A data scientist must be capable of making the best out of the data at hand by identifying and discovering multiple approached towards business growth and potential. Having a sound business domain knowledge helps data scientists easily understand the business problem so that they can choose the appropriate data science model to solve the problem.

ii) Communication Skills

Numbers have an important story to tell. They rely on you to give them a clear and convincing voice.” –Stephen Few

Communication and data storytelling — the most critical non-technical skills for any data scientist. Data scientists should engage business stakeholders such that they can grab their attention both logically and emotionally. A good data scientist must be a great data story-teller so that he/she can present the right information in the right format at the opportune time.

  • Get a Mentor and Build a Portfolio of Data Science Projects

To land a data scientist gig, you will need a portfolio of various data science projects, so hiring managers can review your work, get a proof of evidence of your data science skills, and assess your commitment to work. If it’s your first data scientist job, you won’t have a data science work portfolio but you can create one and populate it with multiple data science projects. You can start coding on these data science projects and have an industry expert as your mentor and tutor who can help you out while working on these projects. Having a mentor you can turn to when you hit a major roadblock while working on projects is of great help. This seems a great idea but finding a data science mentor is really hard and not everyone knows someone who has worked as a data scientist. If you are finding it difficult to find a mentor, check out Springboard’s Data Science Career Track where you will be paired with a mentor to work on 14 different data science to build a professional data science portfolio.

Recommended Reading

How to build a Data Science Portfolio?

  • The Data Scientist Job Search — Land the Interview, Ace the Interview

If you have done everything as mentioned in the data science career path above so far, no doubt you will meet the requirements for most junior data scientist jobs. Now it’s time to apply to some data science jobs and nail the interview. Make your data science resume shine by ensuring it is up-to-date, simple to read, and free of mistakes. Springboard’s Career Team offers resume reviews and helps ensure that you’re presenting your data science skills to recruiters and hiring managers in the best way possible.

Prepare and practice for each data science interview by referring to some of the most common types of data science interview questions to get a knick-knack of the interview process.

  • Keep Learning

Learning the right data science skills, engaging with peer data scientists in the field, and getting up-to-speed is how you get the data science job, but your efforts should not stop once you land a data scientist gig. Successful enterprise data scientists embrace lifelong learning, and that’s the mindset required to get into right away.

  • Keep yourself up-to-date with the latest tools and technologies in the data science space to advance your skillset.
  • Try Meetup, GitHub, and Reddit and join an established data scientist group in your local area.

Where to go from here to become a great Data Scientist?

We hope that now you are ready to take off on your data scientist ambitions. You now know how to become a data scientist. The rest is your decision. If you want to learn data science, then the only person stopping you from becoming one…. is you!

“The journey of a thousand miles begins with one step” — Lao Tzu

2019 is an amazing time to advance in this field and take the first step towards becoming a data scientist. Are you ready to take the first step? Yes? Then check out the most comprehensive job-guarantee data science course we have right here at Springboard and let us know what you think!

What’s Next?


Springboard offers a career-focused and job-guaranteed data science course. Begin your Data Science Journey


Excited and curious to know what you learn in Springboard’s Data Science Course? See the Data Science Course Curriculum


Have questions about the Data Science Career Track? Contact our career coach at +917483024691 or india@springboard.com

Remember, anyone can become a data scientist. The most difficult part is getting started with learning data science, so choose a great mentored data science course and dip your toes in the world of data science today. Trust me- you won’t have to look back!

Source: Artificial Intelligence on Medium

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