How to Become a Data Scientist
Data science educator Jose Portilla provides this definitive guide on becoming a data scientist, which includes everything from resources for acquiring specific skills, to searching for the first job, to mastering the interview.
Hi! I’m Jose Portilla and I’m an instructor on Udemy with over 250,000 students enrolled across various courses on Python for Data Science and Machine Learning, R Programming for Data Science, Python for Big Data, and many more.
Almost every day a student will ask me some form of this question:
In this post, I’ll try my best to help answer this question and point to resources that can help guide you to an answer, also hopefully this post serves as something I can quickly link to my students 🙂
I’m also currently writing a book on acing data scientist interviews! Check it out here. Now on to the rest of this post! I’ve broken down the steps into some key topics and discussed helpful details for each.
If you are interested in becoming a data scientist the best advice is to begin preparing for your journey now! Taking the time to understand core concepts will not only be very useful once you are interviewing, but it will also help you decide whether you are truly interested in this field.
Before starting on the path to becoming a data scientist, its important that you are honest with yourself about why you want to do this. There are probably some questions you should ask yourself:
Ask yourself these questions and be honest with yourself. If you answered yes, then you are on your way to become a data scientist!
The path to becoming a data scientist will most likely take you some time, depending on your previous experience and your network. Leveraging these two can help place you in a data scientist role faster, but be prepared to always be learning! Let’s now jump to discussions on some more tangible topics!
The main topics concerning mathematics that you should familiarize yourself with if you want to go into data science are probability, statistics, and linear algebra. As you learn more about other topics such as statistical learning (machine learning) these core mathematical foundations will serve as a base for you to continue learning from. Let’s briefly describe each and give you a few resources to learn from!
Probability — is the measure of the likelihood that an event will occur. A lot of data science is based on attempting to measure likelihood of events, everything from the odds of an advertisement getting clicked on, to the probability of failure for a part on an assembly line.
For this classic topic I recommend going with a book, such as A First Course in Probability by Sheldon Ross or Probability Theory by E.T. Jaynes. Since these are textbooks they can be quite expensive if you buy new directly from amazon, so I suggest looking at used copies online or at pdf versions to save yourself some money!
If you prefer learning through a video format, you can also check out Khan Academy’s video series on probability. You can also check out MIT’s OpenCourseWare lectures on Probability and Statistics. Both can be found easily for free on Youtube with a simple search.
Statistics — Once you have a firm grasp on probability theory you can move on to learning about statistics, which is the general branch of mathematics that deals with analyzing and interpreting data. Having a full understanding of the techniques used in statistics requires you to understand probability and probability notation!
Again, I’m more of a textbook person, and fortunately there are two great online textbooks that are completely free for you to reference:
If you prefer more old-school textbooks, I like Statistics by David Freedman. I would suggest using this book as your main base and then checking out the other resources listed here for deeper dives into other topics (like ANOVA).
