So, here goes nothing.

Since the beginning of March, I’ve been working my full-time job from home (WFH) due to all this COVID-19. These WFH circumstances consequently give you an ample amount of time, in comparison to when you are at your workplace. Now that I had some spare time, I used it to reflect on my career.

While thinking about my career, I’ve always been fascinated with the field of Data Science but was always discouraged by thinking that it is only for people who are seasoned statisticians, or mathematicians, as the roots of data science go deep in these two fields. And trust me, I’m neither. But still, I wanted to give it a shot.

During this WFH period, my employer-sponsored our learning through coursera.com, which is a blessing in disguise. Now the only thing left is a will to talk the first step. I started scouting for programs on Coursera and also started researching what I needed to get started.

My first stop was a youtube channel by the name of Ken Jee (this). This guy breaks it down and gives it to you straight about how to start your learning. Which courses to take, which projects to pursue. So I started a marathon on this channel, and it was worth it. He sets your expectations and tells you to do’s and don’ts.

Secondly, from my course scavenging on Coursera, I drilled down to the following three courses, which I think should get you started from scratch. The courses range from beginner to intermediate difficulty. I did not follow the below order, but I learned it the hard way that I should have followed the below order:

The first course will get you started about Python. Which, in my opinion, is a better choice for data science professionals? The instructor goes forward with the route taking baby steps for anyone who is not even a programmer. So, by the time you finish the course, you have walked through from basics to intermediate level concepts in Python programming.

The second course gives you an overview and basic concept of Math involved in data science and machine learning. The instructors will show you how to solve the equations on paper, which you will eventually do by running a single line of code. This course includes everything from Venn diagrams, to slops, and will beautifully end at Bayes’ Theorem. The thing is data science is about Maths, and you cannot avoid it. It’s that simple.

The third specialization comprises of 9 different courses, including a Capstone project in the last. For completing this, you achieve a professional certificate from IBM. Each course spans multiple weeks, anywhere from 3 to 6 weeks. It all depends on how fast and efficiently you can absorb and understand the knowledge. The course includes graded quizzes and peer-graded assignments. Because IBM offers the course, you will mostly be working on Watson Studio for your graded and ungraded assignments. The assignments are Jupyter Notebooks, which you have to execute directly in Watson Studio, through the browser.

Though you can do all of it through the browser, eventually, you will need to set up an essential learning environment on your PC or laptop. I configured my laptop with all data science and machine learning essentials. My setup started with simple Python installation, Atom editor, and Jupyter Notebook. For the Python libraries, you will have to follow the flow of the courses, and install the libraries as you progress along. I have a decent laptop with Core i7, 16GB RAM, and an ample amount of SSD space.

Now coming back to Coursera, it is a beneficial learning MOOC (Massive Open Online Courses) platform. You will find industry leaders and some of the top universities offering the courses and professional certificates. All the available courses are free to Audit, which means you cannot earn certificates or grades on them, but you can view all the courses. In case you opt to receive the certification, you will have to pay a flat per-month fee. Now it’s up to you how long you will take to complete your courses.

The above courses are what I thought as the best selection for starting from zero in the field of data science. But if you explore Coursera or any other MOOC platform as well, you will find multiple paths to achieve your goal. All it needs is your will to move forward.

I hope all this information was helpful. Reach back to me through comments if you have any questions. I will be publishing more of my work, assignments, and projects in the coming days.

Stay tunned.