Becoming a Data Scientist is an in-demand career
path. It
requires deep expertise in Data Science and a
strong network to carry you along. Here are some resources to help you on your journey.
Demand for experts in Data Science is growing rapidly. Companies are
looking for people with deep expertise in the field of Data Science to help
them
build their products and services.
As a result, Data Scientists are in high demand and command high salaries. According to leading
sources,
the median salary for a Data Scientist is $120,000 and a
senior Data Scientist can earn up to $140,000. Even entry-level
positions can command great salaries.
No wonder that interest in a career in Data Science is growing rapidly.
Explore the
resources below to learn more about how to become a Data Scientist.
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
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.
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.
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.
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.
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.
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
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.
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.
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
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.
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.
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.
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!
Hui Xiang Chua is a Data Science for Social Good fellow and has over six years of experience solving problems using data in the public service. Combining her passion for education, data, and tech, she was a recipient of the KDD Impact Program award for bringing data science into a …
Data Scientist
at Breakthrough Data Science
5+ Years Experience in Mentoring Data Science, Proven Track Record Landing Jobs for Students
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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 …
Freelance Data Scientist and Data Science Instructor
at Fiverr, Data Society
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Mentored 8 mentees to raises or jobs in the past year Elliot is a freelance Data Scientist with 7+ years of experience, including working with an Olympic team and creating an NBA salary model for Hazan Sports Management, an NBA sports agency. He co-founded a soccer computer vision company that …
Please note: If the technology side of Data Science is your main interest, please consider connecting with other talented individuals on the platform. I can help you to become a strategic partner instead of someone that needs to be managed. Do any of the below statements ring true to you? …
Sr. data scientist | TOP 8 inventors in MENA | Phd. ML/DL
9+ years of experience in AI, ML, DL, research and innovation
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Hello! Check my video first 🙂 I’m a research scientist with a deep-seated passion for making sense of data and a flair for solving complex puzzles in AI 💛. With over 8 years in the field, I’ve tackled challenges in cybersecurity, biology, healthcare, and even manufacturing, transforming data into actionable …
Sr. Data Scientist
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A Decade of Foraging and Forging Data Stories
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I'm Srik Gorthy, a dynamic data scientist with a flair for transforming ideas into AI-driven realities at global leaders like TikTok, Google, and AMD. My journey is fueled by a quest for innovation, from BITS Pilani to Northwestern University, crafting algorithms that empower and inspire. Join me in navigating the …
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.
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.
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!
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!
Practical tips on how to position yourself for a promotion to senior data scientist, including experience, networking, and ways to demonstrate your skills.
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.
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.
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.
Still not
convinced? Don’t just take our word for it
We’ve already delivered
1-on-1 mentorship to thousands of students, professionals, managers and executives. Even
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