Wait! “did someone tell you that, you can become a Data Scientist/ Machine Learning Engineer without coding knowledge?” — A debatable topic however, it is good to have knowledge of coding as this is often what’s going to get you past the Interviews & strengthen your candidature!
- Learn about computer science fundamentals. You can directly jump to learn any of the programming languages but good to have a base understanding of the field/subject & you can visit CS50x to have a good start. For verified certification visit edx for some price but have an option available for financial aid (search for it online on how to get this).
2. Choose any of the preferred languages.
- a) Open Source: Python/R, etc. Usually suggest python, start with as it is user friendly & has good libraries available with strong community available for help whenever stuck. But R is more focused when the analysis is inclined towards statistics more. Decide as per the industry/target companies you would like to work with or have your own start-up, the choice is yours.
- b) Commercial: SAS, SPSS, etc.
3. Where to learn to program from? Well, here I can’t guarantee you any justice as there’s a plethora of information available in this regard. I Can suggest a few to you but, do not limit yourself to these options & at the same time not be overwhelmed with the content. Just choose wisely & follow the path entirely.
4. Most importantly: Practice! Practice! Practice!, Yes, you guessed it right; you can get knowledge about technology via course but, working on the projects adds a feather to the cap.
a) Use the various online platform to learn & practice the code, a few of these include. (Not in any specific order)
- Use GeeksforGeeks or W3School for clearing the basics concepts.
- Refer Codeforces & solve problems based on the difficulty level (easy>>medium>>difficult). Also, other websites you can refer to are FreeCodeCamp | Hackerrank | HackerEarth | Leetcode | Codechef. Remember not to indulge in multiple websites, choose as per your understanding & style of learning from the options available & build one strong profile to showcase rather, having an average profile on multiple websites.
Note: “I am providing some additional resources so, you do not spend more time reading blogs about resources, etc….. So, be very cautious & spend a considerable amount of time in sorting the resources as per your goals but once started with, try to avoid any divergence & stick with the track”- All the best with it.