How to become a Data Scientist

There is a global shortage of data scientists in the industry! Enter the world of stats, maths and data & bring valuable insights to the world's top corporations!

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Why should you become a 
Data Scientist?

Data Scientist

Data Scientists are more in-demand than ever! While many folks in the data space try to get their hands dirty with AI and Machine Learning, they are leaving a big gap in the job market for data analysts and scientists.

In the US today, over 3,000 new job postings for Data Scientists are found. The space is growing and getting more open for career changers.

A Data Scientist can expect to be among the top range of tech salaries. Well into the six-figures in the US on average, and at top ranges all around the world!

Table of Contents

Best books to build Data Science understanding.

A well-written and thorough book can be an amazing path to build deeper understanding and also act as a handbook as you discover the internet's vast resources.

These are our and our experts top picks to get started building career-relevant skills.

Python Data Science Handbook

Python Data Science Handbook

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.

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Introduction to Statistical learning

Introduction to Statistical learning

ISL is a fundamental book and popular amongst undergrad and grad students for its clarity and simplicity with explaining concepts. The math required to understand the book is kept to a minimum, making it unique in its format.

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Head First Statistics

Head First Statistics

Whether you're a student, a professional, or just curious about statistical analysis, Head First's brain-friendly formula helps you get a firm grasp of statistics so you can understand key points and actually use them. Learn to present data visually with charts and plots; discover the difference between taking the average with mean, median, and mode, and why it's important; learn how to calculate probability and expectation; and much more.

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R for Data Science

R for Data Science

This book will teach you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data science.

Just as a chemist learns how to clean test tubes and stock a lab, you’ll learn how to clean data and draw plots—and many other things besides.

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Statistics for Data Scientists

Statistics for Data Scientists

Statistical methods are a key part of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.

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Courses to deepen your Data Science skills.

These days, courses are no longer a sequence of videos. They are usually accompanied by projects and a learning community, keeping you accountable and on the path.

Our experts recommend these courses, from free selections to paid programs.

Fast.ai

Fast.ai

With the motto "making neural nets uncool again", fast.ai is a straight-to-the-point practical (and free!) course that is valued by Machine Learning enthusiasts and engineers worldwide. Fast.ai comes with a community, many practical projects and great content.

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Machine Learning A-Z™

Machine Learning A-Z™

Kirill Eremenko's course on Udemy is a classic with almost a million (!) students worldwide. A-Z takes you from a bit of coding knowledge to making your own predictions and building ML models pretty swiftly. At prices between $10 - $20 it's also cheaper than many alternatives.

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MIT Open: Linear Algebra

MIT Open: Linear Algebra

Math is the foundation of Machine Learning and much needed if you need to work on the inner logic of its systems. Senior engineers are encouraged to propose and submit their own papers – and getting your LinAlg back in order is a must for that.

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Lex Fridman's MIT Deep Learning

Lex Fridman's MIT Deep Learning

Lex Fridman is the instructor of an immensely popular and fundamental Deep Learning course at MIT. Together with the other MIT AI courses, this can help polish your skills and get the foundations right.

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Data Science A-Z

Data Science A-Z

Learn Data Science step by step through real Analytics examples. Data Mining, Modeling, Tableau Visualization and more!

Successfully perform all steps in a complex Data Science project, read statistical software output for created models and receive professional step-by-step coaching in the space of Data Science

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Harvard Online Data Science

Harvard Online Data Science

To become an expert data scientist you need practice and experience. By completing this course you will get an opportunity to apply and gain knowledge in R data analysis. This final project will test your skills in data visualization, probability, inference and modeling, data wrangling, data organization, regression, and machine learning.

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Get the guidance you need to become a Data Scientist

There is no better source of accountability and motivation than having a personal mentor. What used to be impossible to find is now just two clicks away! All mentors are vetted & hands-on!

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Mohit Khanna 🇮🇳

Data Scientist at Google
5.0 (29)

I am a Data Scientist with 8+ years of rich industry experience currently working in Google on the Google Maps team. Me and my team works on building counter abusive models to keep Maps free from abuse. I completed my Masters from UIUC in CS with a specialisation in Data …
Engineering & Data Data Scientist Machine Learning Data Analyst Data Engineering ETL SQL
$110 / month
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Mert Ozlutiras 🇩🇪

NLP Engineer / Data Scientist at Startup
5.0 (1)

👋 Hey, I'm Mert, and I've been right where you are in the tech world. I started in a non-technical role in Turkey, and the journey to where I am today, working with NLP tech and generative AI in Germany, wasn't always a smooth ride. I know it's full of …
Engineering & Data Natural Language Processing Data Science Data Machine Learning Artificial Intelligence Data Analytics
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Leoson Hoay 🇺🇸

Data Scientist at Learning Collider
5.0 (21)

Hello there! I am a Data Scientist at Learning Collider, previously Tech Lead at the University of Chicago Urban Labs. I am passionate about leveraging technology to solve human problems and to advance social good, and have many years of experience in hiring for data teams, and extensive programming and …
Engineering & Data Data Science Python Machine Learning Technology Management Data Scientist Data Analyst
$120 / month
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Benjamin Bell 🇺🇸

Data Scientist at Breakthrough Data Science
5.0 (13)

I know what it’s like to try to break into data science. In September 2014, I drove to San Francisco from my home in New Haven, CT to attend a data science bootcamp with dreams of landing a job in the field. Two data science positions later I began teaching …
Engineering & Data Data Science Machine Learning Natural Language Processing Statistics Python Data Analysis
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The Data Science must-reads you shouldn't miss.

Key articles and posts of industry experts can help you get a better picture of what you are getting into.

In our opinion, these are some must-reads you really shouldn't miss.

Simple Reinforcement Learning with Tensorflow

This 8-part series by Arthur Juliani (Deep RL researcher at Unity) is an amazing entry point to the new and mysterious advancements of Reinforcement Learning, perfectly suited for folks coming from other topics in Machine Learning.

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Karpathy on "Software 2.0"

Andrej Karpathy is the Director of AI at Tesla. Before that, though, he authored this blog post in 2017 talking about Deep Learning as "Software 2.0" of some sort. A must-read if you ever want to have another way of thinking about ML.

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Opportunities and projects in the Data Science space.

In the end, advancing your career is all about getting the right opportunities at the right time and a good portion of luck.

These are some interesting things going on in the Data Science space and you probably don't want to miss them.

Specialize with Kaggle

It wouldn't be the first time I've seen someone get hired over good Kaggle results! Kaggle competitions are data science and ML projects that are graded through a public leaderboard. A good place on the leaderboard shows that you know your craft and can apply your knowledge to real-life problems!

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Get into open-source

The world thrives on open-source software and this is no exception. Core contributors to core libraries and fast-growing tech like React, scikit-learn, Bitcoin and TensorFlow prove their abilities by going into the inner workings of a framework to improve it. For many companies, that's a desirable skill!

These projects are always looking for fresh faces. Grab an issue from the issue board or review a PR to get started!

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Tips from the Data Science blog

Learn more about Data Science with our expert advice.

Regex 101 For Data Scientists

Understanding the basics of regular expressions is essential for any data scientist. In this blog post, we'll cover the fundamentals of regex and how to use them in data science applications.

Physicist Turned Data Scientist I: A Path from Academia to Industry

This is my personal story on transitioning from academia as a Physicist to industry as a Data Scientist. If you are on the same boat and want to know the process and every step in the way, this note is for you. I hope you enjoy and find it useful for your career growth.

How to become a self-taught Data Scientist

Data Science is one of the most trending career paths right now, but it’s also one of the most competitive ones to get into. With hundreds of master students and PhDs entering the workforce every year, career changers and starters may get discouraged.

Get the help you need & advance your career

Ready to enter the competitive world of Data Science? Our mentors can help, whenever you are getting lost!

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