ADVICE - GENERAL
I receive many questions regarding what to do during university education to be a competetive candidates in the future. Here are some tips I can recommend:
Have a fluent English. And second language is a plus. I use Duolingo for myself to learn a second language.
Try to extend your (qualified) network of people. Qualified people will help you to find good internships, jobs, educations and information about all these. Most of you know how to extend your network better than me: seminars, club meetings, social events,...
Try to understand where do you want to be (and you DON'T(!) want to be) in the future. In a production plant, in a bank, in a FMCG company, in a university, in a municipality... The best way of finding this out is (1) to have experience, i.e., internships and (2) listening to experiences like club events.
Data science and machine learning are very popular for the last 5 years. If you are interested, it's good to learn Python and R and also some database skills like SQL.
Master one of the languages. Don't try to learn Java, C++, HTML etc at the same time. Python ( I prefer it) or R are very good candidates.
There many online courses that teach coding, data science and machine learning. I cannot say this is the best, but I will give a list of online learning sites at the end of this note.
Don't forget to make physical exercise! There are many scientific researches that show positive effects of regular exercise on (1)daily brain actions and (2) diseases like alzheimer and parkinson. Here is a TED talk from Prof. Wendy Suzuki about exercise.
We need honest, virtuous and moral people more than anything else. Hence before being a data scientist or an engineer, be honest, virtous and moral.
Invest in yourself. There are many online courses for learning. Here is a good review:
Here are some that I know:
StackExchange: You can find a lot of answers to your questions in this site.
For those who are interested, Kaggle is an interesting web page which has many competitions about data science and machine learning
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Before asking a question (especially to our instructors), please think about the potential answers so that you can design your question better.