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!
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!
Best books to explore the Data Science career
path.
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 on the Data Scientist career path
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!
I am a data scientist with experience in creating insights from data, making analyses, building predictive models. I love to impact while getting impacted on. I look forward to building a community of data-aware individuals and communities who would use data to solve the unique problems of their community.
Sr. Data Scientist
at TikTok(ByteDance Inc.)
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 …
Lead Data Scientist
at Meridian Energy
Eight years experience mentoring data professionals to get jobs
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Hi - I'm Adam, a data professional, with experience across all the three main data professional roles - data analyst, data scientist and data engineer. I have eight years of teaching & mentoring data scientists, both teaching at a data science bootcamp and one-on-one mentoring. I'm currently working as a …
Manager-Data Scientist
at McDonald's
10 years of experience in Data Analytics and Data Science
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I've helped 13 mentees land job offers or career advancements. In 2024, 6 mentees received DA/DS offers! 🎉 Hello! 👋🙂 I'm a professional in Data Science, Machine Learning and Business Analytics for fortune 500 companies, including financial services and retail services. 💻🤖 During my 10-year experience in the US corporate, …
Lead Data Scientist
at United Health Group Inc (OPTUM LABS) || EX-ORACLE
Lead Data Scientist working with United Health Group Inc || Ex-Oracle India Private, Ltd, Ex-IBM
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Tanvi is a Senior Data Scientist working with United Health Group Inc and worked with Oracle India Private, Ltd. She contributed to the Research &Development of Oracle Cloud Infrastructure (OCI) that enables customers to build and run a wide range of applications. She has also contributed to Infiniti Research, the …
Lead Data Scientist
at Amazon
Renowned AI Mentor with 13 years of experience in AI,MAchine Learning,Data Science and Analytics
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With an extensive 13-year career in the world of data analytics and data science, I am an accomplished Lead Data Scientist currently spearheading initiatives at Amazon. My career is marked by a deep and persistent dedication to leveraging data to derive actionable insights and build data-driven solutions. After obtaining a …
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!
Growing into a Senior Data Scientist role pays off –
$20,000 up to per year.
Not only is the progression from individual Data Scientist to a Senior role a natural
progression, it
also makes all the difference in your average salary.
On a global average, Senior Data Scientist receive a
$20,000
salary hike over entry-level Data Scientists when they go from individual contributor to
senior personnel.
