/ Data Science

How to Become a Data Scientist (2021 Guide)

Are you looking to join the lucrative and exciting field of data science? Data scientists have become one of the most in-demand jobs of the 21st-century economy and are needed in almost every single industry. Data scientists regularly report high salaries and high job satisfaction. So you've probably already asked yourself: how do you become a data scientist?

If you're preparing for a data science interview or looking to prepare for future interviews check out our data science interview course here.

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How to Use This Guide

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This article is composed of several sections, each of which is dedicated to an aspect of becoming a data scientist. A brief overview is offered so you can get familiar with the particular topic, but each is accompanied by a series of links to other articles or resources that tackle each aspect of the topic in more depth. We highly recommend that you visit the links and return to this page to continue to learn more about data science.

Think of this article as your base of operations or your staging area when it comes to learning how to become a data scientist. Each section is meant to help you get a broad overview of one particular part of becoming a data scientist, while also offering you additional articles and resources to dive deeper, before returning to this page. So let's get to it.

What is the Role of a Data Scientist?

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1987
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Before we go any further, let's clarify something. What is the role of a data scientist, anyway? What do they actually do? Simply put, data scientists work with other business stakeholders on how they can use data to best achieve the goals of a company. Their role, then, would entail collecting, modeling, analyzing, and presenting that data while building machine learning or predictive analytics models so that a company can have insight into the future. The role ultimately comes with many different hats and responsibilities. We explain each in more detail in several other of our articles. Be sure to check them out!

Learn more:

What Does a Data Scientist Do?

Data Analyst to Data Scientist

Data Scientist vs. Data Analyst

Emma Yee: Analyst to PM

How to Become a Data Scientist

Front-End Development
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Get an Education

First and foremost, it can't be understated how important education is to become a data scientist. Data science isn't considered one of the sexiest and most lucrative jobs of the 21st century because it's easy or simple. There's no way around it: data science requires a significant amount of education.

Now, that doesn't necessarily mean it requires formal education. It is possible to learn a significant amount of data science from online courses and bootcamps. There are countless books you can read that will give you a wealth of knowledge, as well.

Yet, the multidimensional value that comes with formal academic education can not be discounted.

An Academic Data Science Education

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Data Science is an incredibly complex and rigorous discipline. The high salaries associated with most data scientist positions are such because the field requires a substantial amount of training and a deep understanding of highly advanced topics in statistics, mathematics, programming, and artificial intelligence. All of which are vigorous and difficult subjects on their own.

The truth is: the most straightforward path to a data science career is by first receiving a formal education in IT, computer science, mathematics, statistics, or a related field.

Learn more:

Landing College Internships: Engineering, Growth, and Product Management

The Top 10 Schools to Study Data Science

Best Big Data Degrees

Best 23 Schools with Data Science Master’s Program

Attend a Bootcamp

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That's not to say universities are the only places you can learn data science. Nor could it be the end of a high-quality data science education. There are many data science bootcamps on the market right now that can provide their students with perks and benefits that could be invaluable in building their data science careers.

Bootcamps are a great place to not only get a firm understanding of the foundations of data science but into the cutting-edge and the latest innovations in the field. Most graduate their students with a professional and impressive portfolio, mock interviews under their belt, and access to their large professional networks. All of which could be instrumental in an aspiring data scientist's career.

   Learn more:

The 5 Best Data Science Bootcamps

These 5 Coding Bootcamps Are Worth Considering

Lamda School Review

Take Online Courses

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Many online courses and MOOCs (massive open online courses) are now available to those looking to learn how to become a data scientist. Whether you're dipping your toes into the discipline for the very first time, or just looking to sharpen your data skills, there's an online course for you.

Here's our list:

Cognitive Class

Coursera

See all Coursera data science courses here.

EdX

See all EdX data science courses here.

Udacity

See all Udacity data science courses here.

Khan Academy

Learn the Necessary Skills

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What skills do aspiring data scientists need to bring to the table to get the job offer? Because data science is an inherently interdisciplinary field, there are several skills that data scientists need to have on their resume to obtain positions at the top companies.

Stats

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Statistics is one of the cornerstones of data science, being the field that deals with collections of quantitative data. Every data scientist needs to have a good understanding of statistics to perform at their positions. Not only that, but they also need to have an understanding of the similarities and differences between the two disciplines, as they are separate fields, despite their overlap.

Learn more:

Machine Learning

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Machine learning and artificial intelligence are one of the most powerful tools in the data science arsenal. Oftentimes, the data sets that data scientists use are so massive or complex, that it would be impractical for a human being alone to make sense of it. Computers, however, are perfectly poised to find useful patterns and trends out of these massive data sets.

Aspiring data scientists, then, should be comfortable with building machine/deep learning models to become a data scientist.

Machine Learning as a Service (MLaaS) for data scientists

With the advent of cloud computing technologies, many data scientists will most likely utilize the machine learning capabilities of cloud service providers such as Microsoft's Azure, Google Cloud, or Amazon Web Services to build their ML models and conduct their data experiments. If you're looking to become a data scientist, it's wise to become familiar with the machine-learning-as-a-service (MLaaS) platforms offered by the top cloud computing companies.

Amazon Web Services (AWS)

Source: Wikimedia

Data Science Tools - Amazon Web Services

Machine Learning: Data Scientist - AWS

Predictive Data Science with Amazon SageMaker and a Data Lake on AWS

Google Cloud

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Google Cloud AI and Machine Learning Products

AI and Machine Learning Solutions

AI Workshop

Microsoft Azure

Source: Wikimedia

Azure AI Cognitive Services

Azure Machine Learning

Azure Bot Service

Programming

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At this point, you probably know that programming is a prominent skill for data scientists. Nearly every job posting for a data science position will require some past experience and comfortability with programming. While dozens of contemporary languages are frequently used in the workplace, data scientists usually use one, or both, of the following:

R

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R is a programming language frequently used by scientists and researchers because of its capabilities for statistical modeling and analysis. As we mentioned, statistics and statistical modeling is a big part of data science, so it should come as no surprise that R is a frequently used language amongst data scientists. Not every data science team may use R, except for those that are focusing on modeling their data. Ultimately, modeling and presenting data is a huge part of the field, so it's recommended that you become familiar with R to become a data scientist.

Learn more:

Learn R - Codecademy

R Programming - Coursera (Johns Hopkins)

Learn R with Swirl

Python

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Python is one of the leading programming languages for data science and beyond. It's simple, with a robust community, and can be used with several frameworks and packages like NumPy, Tensorflow, and pandas that are perfect for big data analytics. Unlike R, Python is the language best suited for machine/deep learning, which is another major piece of the data science portfolio. Python is considered to be the bread and butter for many data scientists, so getting familiar with the language is a must.

Learn more:

Get familiar With the Necessary Frameworks

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In addition to experience with programming, many data science job postings will list several frameworks they prefer their applicants to be familiar with. If you want to become a data scientist, learning some of the following frameworks is a must.

Tensorflow

Scikit-learn

Vis.gl (Uber)

Pandas

Keras

Pytorch

NumPy

Apache Kafka

Matplotlib

Learn Data Visualization

Crunching the numbers
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Data visualization is another crucial skill to learn before you become a data scientist. We mentioned in our article, "What Does a Data Scientist Do?" that an important part of any data scientist position is data storytelling and the presentation of data. As such, data scientists need to know how to visualize and present their findings and predictions so that the stakeholders involved can understand the, oftentimes, complicated story told by data sets.

Google Charts

Tableau

Grafana

FusionCharts

Chart.js

Sigma.js

Polymaps

Learn Data Mining

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At the end of the day, no data science can be done without first acquiring and organizing data sets. This is often done by a process called data mining, which is essentially combing through multiple different data sets to find any patterns or insights which go on to be analyzed by data scientists. This is another must-have skill for anyone trying to become a data scientist.

Learn more:

Everything You Need to Know About Data Mining and Data Science

The 10 data mining techniques data scientists need for their toolbox

45 Great Resources for Learning Data Mining Concepts and Techniques

Data Mining Tools

5 Myths of Data Mining

Touch up your Soft Skills

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Although data scientists may be math and numbers people at heart, there's still a big place for soft skills in the data science world. Data scientists almost always interact with people outside the data team, so the most effective ones have sharp communication, creativity, problem-solving, and strategic skills that they will use throughout their careers. Don't forget to touch up on your soft skills before your data science interview!

Learn more:

Soft Skills for Data Science

Soft Skills Will Make or Break You as a Data Scientist

Soft Skills Every Data Scientist Must Possess

The Soft Skills That Are An Asset to Every Data Scientist

Read Some Data Science Books

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Another great way to learn data science is by reading some of the many books dedicated to the subject that are on the market today. Given the popularity of data science in today's marketplace, you shouldn't be surprised to find out that there are countless books that are high-quality and very informative. Here are some of our favorites:

Stats & Math Books

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Practical Statistics for Data Scientists by Peter Bruce

Introduction to Statistical Learning by Gareth James

Think Stats by Allen B. Downey

Introduction to Linear Algebra by Gilbert Strang

A Probabilistic Theory of Pattern Recognition by Luc Devroye, Gábor Lugosi, László Györfi

Mathematics for Machine Learning by Marc Peter Deisenroth

Machine/Deep Learning Books

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Learning From Data by Yaser S. Abu-Mostafa, Malik Magdon-Ismail, Hsuan-Tien Lin

Machine Learning with TensorFlow by Nishant Shukla

Programming Collective Intelligence by Toby Segaran

Deep Learning (Adaptive Computation and Machine Learning) by Ian Goodfellow, Yoshua Bengio, Aaron Courville

Deep Learning with Python By Francois Chollet

Data Science Tool Books

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Data Analysis with Open Source Tools: A Hands-On Guide for Programmers and Data Scientists by Philipp K. Janert

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurelien Geron

Practical Data Science with R by Nina Zumel, John Mount

Spark: The Definitive Guide by Bill Chambers, Matei Zaharia

A Guide to MATLAB: For Beginners and Experienced Users by Brian R. Hunt, Ronald L. Lipsman, Jonathan M. Rosenberg, Kevin R. Coombes, John E. Osborn, Garrett J. Stuck

Big Data Books

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Ethics of Big Data: Balancing Risk And Innovation by Kord Davis

Big Data: Principles and Best Practices of Scalable Realtime Data Systems by Nathan Marz, James Warren

Designing Data-Intensive Applications by Martin Kleppmann

Too Big to Ignore: The Business Case for Big Data by Phil Simon

Big Data at Work: Dispelling the Myths, Uncovering the Opportunities By Thomas H. Davenport

Theory/Philosophy

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Superintelligence: Paths, Dangers, Strategies by Nick Bostrom

Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell

Incerto 4-Book Bundle: Fooled by Randomness, The Black Swan, The Bed of Procrustes, Antifragile by Nassim Nicholas Taleb

Chaos: Making a New Science by James Gleick

Build an Impressive, Professional Portfolio

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An impressive data science portfolio is a must-have for becoming a data scientist. Not only will it help you get interviews, but it will also help you get the offer.

3 Steps To Build A Data Science Portfolio

How to Build a Data Science Portfolio

What Projects You Should Include in Your Portfolio

Everything you need
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Top 3 Datasets for Data Cleaning Projects

4 Steps to Help Kickstart Your Data Cleaning Project

Exploratory Data Project

Best Data Visualization Projects of 2019

Data Viz Project

6 Complete Machine Learning Projects

Top 21 Machine Learning Project Ideas for 2020

Final Projects from Data Storytelling Studio 2018

Visual Data Storytelling Examples to Inspire You to Create Data Narratives

Apply, Interview, and Get the Offer

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The final step in becoming a data scientist is, unsurprisingly, applying for, interviewing, and receiving a data science job offer. If you've received an education, developed the skills, and built an impressive and stunning portfolio, you shouldn't have too much trouble in getting interviews for data scientist positions. The field is experiencing a shortage of qualified candidates, after all. However, you still need to ace the interview to make it to the finish line. Here are some ways you can do just that:

Networking as a Data Scientist

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As is the case for nearly every job, a strong professional network can pay huge dividends for an aspiring data scientist. A connection at a company can easily get your foot in the door, and could potentially make a great first impression for the hiring manager overseeing your interview. If you can make connections with those that can vouch for your value as a data scientist, you'll find that you'll have a much easier time receiving job offers after a successful interview. This is especially true for the huge tech companies such as Google, Amazon, Netflix, Facebook, Microsoft, and others who may have highly competitive workplace cultures and receive applications from thousands of candidates. If you're looking to become a data scientist, don't neglect your professional network building.

Finding the Right Data Science Mentor [Checklist]

What Every Aspiring Data Scientist Needs to Know About Networking

Building your Data Science Network

Ace the Data Science Interview

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While a friend or colleague may get your application to the top of the pile, you won't receive a job offer without acing your data science interview. But don't worry. There are a couple of things you can do beforehand to improve your interview skills and better your chances of sealing the deal.

Mock Interview

Mock interviews are huge. Data science interviews can be a little complicated, so it's a wise idea to practice a run-through before the big day. That way, you can iron out any kinks or deduce what areas, if any, you may need to improve on before the real thing. Interviewing is a skill, and like any skill, it requires practice to do well. Don't leave your data science career to chance.

Get 1:1 Expert Mock Interview DS Coaching With Exponent

Data Science Interview Prep

Preparing for the Data Science Job Interview

Interview Coaching

But it doesn't stop there. It may be difficult for you to determine what areas need some improvement and what areas you do well. Everyone tends to have a hard time estimating their own abilities. This is why we recommend getting a data science interview coach that'll tell you exactly how you perform, what you should work on, how an interviewer will perceive you, and so on.

Book a Session With Exponent's Data Science Coach, Matt Strauttman.

How an Interview Coach Can Help Your Job Search

15 Teleconferencing Tips for a Successful Remote Interview

Data Science Interview Questions: From Screening Through On-Site

Join the Exponent Data Science Community

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Last, but certainly not least, we recommend becoming a part of the Exponent community. There you can join hundreds of other data scientists, product managers, program managers, and software engineers who support each other in their individual employment journeys. Sign up for Exponent's data science interview course to not only get access to hours of useful content but access to our exclusive Slack community composed of aspiring data scientists like you, along with expert coaches and industry professionals.

Ace your Data Science Interview
Anthony Pellegrino

Anthony Pellegrino

I’m a rather bohemian freelance journalist and tech content writer. Philosophy/CS student - A.I.,Consciousness, Social Sciences.

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