The 4 Must-Learn Data Science Courses for Absolute Beginners

The 4 Fundamental Courses to Help You Get Started

If you’re an absolute beginner, cut out all the noises on the internet and follow these courses in order. You can do them in parallel too, and if you stay on the schedule, you shouldn’t take more than 6 months. All 4 of the below-mentioned courses are free to view and only require payments to get certified.

1. Applied Data Science with Python

Offered by the University of Michigan, this course teaches Python in a data science focussed manner. It goes from data wrangling to data analysis to visualization to text mining and network analysis. It gradually takes you through a programming journey without diving deep into the theory.

It’s a great hands-on start to get the overall understanding of the machine learning workflow. The exercises and assignments aid the hands-on nature of the course. So give this course a go, and you’ll know what I’m talking about.

2. Statistics with Python

It’ll be tempting to skip statistics but sooner or later, you’d regret it. It was worth all the time I patiently invested in this. Slowly, I started understanding all the statistical concepts.

The course is packed with many examples, case studies, and exercises that are helpful for a beginner. To date, I use these concepts at work, and you must gain clarity on these topics in your early days.

3. Machine Learning by Andrew Ng

When you advance through this course, you’ll feel breaking into data science and machine learning slowly. Many professionals, including me, owe most of our knowledge to this single course.

The only drawback is that the course is back from 2012 and uses Matlab/Octave for the assignments. You can follow assignments in python from the same course available on YouTube.

4. SQL Basics for Data Science

Most people ignore SQL — the language of data until they realize its importance.

Sooner or later, you’ll be required to use SQL heavily in your day-to-day job — some roles are entirely focused on SQL, so you must master it early on. I’ve tried multiple courses, but this one directly focuses on what we need from a data scientist's perspective.

It’s more than sufficient if you work through the first 2 courses of the specialization. The last two are quite advanced and will only be helpful when you start working on big data in a distributed setting.

Here are Some Hard-hitting Truths to Stay Focused in Your Journey

Now there are always alternatives to the above-mentioned courses. There’ll be groups of people arguing over Python vs. R, Projects vs. Courses, Hands-on vs. Theory-First, and which courses are the best for each topic. Here’s the truth about all these thoughts:

• Opinions are biased and based on individual preferences.
• There exists more than one path to succeed in data science.
• Too much information, i.e., Information Overload, makes you overwhelmed and derails you from all paths.
• You need to be focused on at least one path consistently to become a data scientist.
• The path you choose must be a simple one that helps you take action.

This article was focused on the bare minimum you need to get started. I want you to take action without worrying much. So next time one of my mentees gets confused with all the resources out there, I’m going to send them this.

If any candidates find any queries or doubts regarding this post so they can ask our experts. Our team will be solving all your queries in a limited span of time. Thank you. Team ExamHub