It would depend on the time & funds that you can set aside to do the course. If you want to do a full-time course, you will need to do a bit of research. My premise is that you intend to work as a Data Scientist after passing out.
Check which institutes get campus visits of companies for recruitments. If any institute boasts of mass recruiters(for eg Infosys, TCS are mass recruiters for Electronics/IT related profiles-they recruit many students from the same batch), it would be a good choice. The placement records of institutes vary almost every year. Most good institutes publish their placement reports & it’s not difficult to contact a passed out student. Either you seek details from the institute, or you can use social media to connect to get plausible info.
If any institute allows you to work on concurrent projects/live projects → they would probably be a good choice. If you get internship, good. Because chances are you get experience, stand a chance to get PPO(Pre Placement Offer), as well as you get connected to the MI/AL crowd, which could be of helpful later on.
The buzz is that R(tool for D.S) is not enough nowadays, people are migrating to Python. You must check the curriculum. Whether they are going to teach you only R or will teach you extensive use of Python as well. Python is an easy language to learn. You can pick up a lot of free material on the internet to learn Python. Also, check if they are delving deep into AI/ML(Artificial Intelligence/Machine Learning). You will get to know this either via ex-students or by going through the curriculum/syllabus which you may get in the brochure.
Don't go by the publicity done by the institutes. Though the placement stats may vary every year, still there is no astronomical level of change expected. I can take the names of institutes, rank them city wise, but that could influence your ultimate choice. Rankings are dynamic. They are based on numerous factors. The yardsticks which govern your choice could make an institute ranked much lower in a particular ranking as your more preferred institute.
Based on the metrics/constraints you have, make an informed decision. You will have to live with the result that accrues as a result of your decision. But hey, that's what data scientists do- draw interpretations based on data made available.
Good Luck!
Check which institutes get campus visits of companies for recruitments. If any institute boasts of mass recruiters(for eg Infosys, TCS are mass recruiters for Electronics/IT related profiles-they recruit many students from the same batch), it would be a good choice. The placement records of institutes vary almost every year. Most good institutes publish their placement reports & it’s not difficult to contact a passed out student. Either you seek details from the institute, or you can use social media to connect to get plausible info.
If any institute allows you to work on concurrent projects/live projects → they would probably be a good choice. If you get internship, good. Because chances are you get experience, stand a chance to get PPO(Pre Placement Offer), as well as you get connected to the MI/AL crowd, which could be of helpful later on.
The buzz is that R(tool for D.S) is not enough nowadays, people are migrating to Python. You must check the curriculum. Whether they are going to teach you only R or will teach you extensive use of Python as well. Python is an easy language to learn. You can pick up a lot of free material on the internet to learn Python. Also, check if they are delving deep into AI/ML(Artificial Intelligence/Machine Learning). You will get to know this either via ex-students or by going through the curriculum/syllabus which you may get in the brochure.
Don't go by the publicity done by the institutes. Though the placement stats may vary every year, still there is no astronomical level of change expected. I can take the names of institutes, rank them city wise, but that could influence your ultimate choice. Rankings are dynamic. They are based on numerous factors. The yardsticks which govern your choice could make an institute ranked much lower in a particular ranking as your more preferred institute.
Based on the metrics/constraints you have, make an informed decision. You will have to live with the result that accrues as a result of your decision. But hey, that's what data scientists do- draw interpretations based on data made available.
Good Luck!
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