Self-Taught Data Science recipe

I already walked you through my journey as a Self-Taught DS here. The main thing other tutorials and resources lack in my opinion, is that they just have a "generic definition" for Data Science, and that's it! They do not distinguish the different types of jobs that are all advertised as "Data Scientist". That's why you are ended up with a huge list of required skills, which makes it hard (if not impossible) to see yourself as a perfect candidate for Data Scientist positions.

I would like to assure you that you know more than what you need! You just need to be aware of all different types of roles out there in the market (even in Top Five Tech companies!😧), and apply for the ones closest to your skillset and interest.

To really understand what I mean by different types of DS roles, take a look at my other post on "Do Data Scientists really exist?"

The takeaway from the post is: there is not a unique definition for Data Science. You now get a better understanding why you should make sure to develop the skills needed for the "type" of Data Scientist you are interested in. I'll give you a quick 3-ingredient recipe to start learning Data Science! 👩🏻‍🍳

After months of research, I finally made myself a 3-ingredient recipe to start learning Data Science. The material and online tools I am sharing below helped me achieve my goal of landing Data Scientist positions at the Top Five Tech companies. I eventually decided to go with Amazon and am about to start my dream job as a Machine Learning Data Scientist.

1- Start with Math & Stats

Khan Academy is your best bet to learn or refresh your knowledge on Stats and Math to build the solid background needed for DS. The main topics to consider reviewing would be: basic probability and statistics and hypothesis testing. There is also a YouTube channel which makes your life much easier I suppose!

Before I elaborate on the next two, I'd like to repeat another time that be aware of the type of Data Scientist role you are interested in. If you are interested in a BI Analyst role rather, you may not need to learn ML or even Python. There are huge opportunities for BI Analyst roles: although most of these are advertised as Data Scientist roles, you can still tell it is a BI type by looking at the skillset needed in the job description. For a BI Analyst role, you need to practice visualization tools such as Tableau or Power BI.

2- Start Learning Python.

This is your number one reference for Python. The title may not be descriptive enough, but this textbook is the best resource to start learning python or even learning coding from scratch.

You can get this from Amazon or directly.

It teaches you how to use a divide and conquer strategy, which is how this book is written.

You get a step-by-step tutorial on things you need to know to code. There are exercises in the book as well as GitHub pages with solutions to the problems.

Don't forget: Practice, practice, practice!

3- Learn Machine Learning (ML)

To learn ML from scratch or to understand it more deeply, I would recommend the Machine Learning Specialization on Coursera, taught by well-knowledgeable professors from the University of Washington. You can listen to all the lectures for free. Financial aid is also available if you'd like to practice the Python exercises and get the ML certificate.

Takeaway:

These are all fabulous resources to get you started on learning Data Science, however there is still a lot more to learn if you are applying for a specific role such as in Natural Language Processing (e.g. Alexa) or Deep Learning. In addition, although a successful person is always learning and you should study all 3 resources I mentioned above, at some point during your career as a Data Scientist, the concepts being taught in the above resources are still generic and may not all be needed to nail your Data Scientist job interview.

1. I am preparing my own version of prep material by cherry-picking from all the fabulous resources I used, stay tuned!

2. I'd be talking about the impact of certificates in your resume in another post, stay tuned!

Good Luck Data Scientists to Be!