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My Data Science Journey and Suggestions - Part 1

I always wanted to share my detailed Data Science journey. So, I have divided the whole journey from BTech first year to final year into 3 parts. I will share everything, without leaving a single detail, starting from my projects, internships to getting a job. You can follow the path that I have followed if you like my journey or create your own path.

In 2015, I got a seat in Electronics and Communication Engineering (ECE), IIIT Sri City through IIT JEE Mains. Because of my rank in JEE Mains, I couldn’t get into the Computer Science department. I wanted to shift to Computer Science after my first year, but couldn’t due to some reasons. In our college, we have only two branches, CSE and ECE. For the first three semesters, the syllabus was the same for both the departments except for a few courses. This helped me to explore Computer Science.


In the first 3 semesters, I took Computer Programming, Data Structures, Algorithms, Computer Organization, Operation Systems courses, which are the core of Computer Science. Even though I was from ECE, I spent most of my time in coding. It was fun solving coding problems. Parallelly, I was introduced to Frontend and Backend frameworks. The front-end part didn’t excite me much but backend frameworks like Django, which involves a lot of coding in Python was very interesting. I did an additional project under my Algorithms professor, which aims to improve the performance of Dijkstra's algorithm. At that time, I was an algorithms enthusiast.


In the fourth semester, I didn’t get a chance to take the Artificial Intelligence course which was offered as a Computer Science elective. I was unhappy at that time. So, I decided to learn on my own based on the topics that were taught in the course. While exploring, I came to know about Machine Learning course by Andrew Ng. At that time, it was the most popular online Machine Learning course. This was the first step of my Data Science journey. During my three-month Summer holidays break, I completed the entire 11-week course and noted all the main points in a notebook, which helped me a lot during the interviews. My suggestion is to maintain a notebook specifically for Data Science and make a note of all the important points.

If you are entering into Data Science/Machine Learning, I highly suggest you take this course, because the course covers all the basic concepts that are required. Here is the link to the course.


This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition. Topics include:
  • Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks).
  • Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).
  • Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).
The course will also draw from numerous case studies and applications so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas. (Reference: Course page)

The only caveat in this course is that you have to solve the assignments in the Octave language. Octave is similar to Matlab. It would have been better if they have used Python language. Actually, the course was created 10 to 15 years ago and Python was not popular at that time. However, it’s worth doing the course if you want to understand theoretically.

This is the end of Part 1. I hope you enjoyed reading my journey. This will be really useful for beginners. If you are in the first or second year of engineering, you can start with the above course. Don’t waste time searching for resources. If you have any queries, comment in the comments section below. I would be more than happy to answer your queries.

Link to the Part 2 of my Data Science journey.

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