1. Learn to love data
You need some motivation to discover the connection between statistics, linear algebra, and neural networks , to fall in love with the sense of accomplishment that data gives you, not to have a headache when you see it. The great thing about data science is that there are infinitely interesting things to discover - that is asking questions and finding a way to get answers.
2. Learning by doing
Get a type of data that interests you, analyze it, such as mapping all devices on the Internet, finding the location of real NBA players, where there are refugees this year, or something else. In this hobby, the grammar that you can't understand even if you just flip through the book and read the obscure words, maybe you can learn it while you are analyzing and rummaging through the actual operation. Practice will bring out the truth, the older generation will not lie to us
3. Learn to communicate
Part of communication is understanding and theory of the subject, another is understanding how to structure your results. The last part is being able to explain your analysis clearly. You must know that there is nothing powerful about what you understand, and your data results can be understood by people who do not know this aspect, and that is your power. Just a little advice, start blogging logically, no matter what, start with what you are familiar with! As long as you can get into the habit of writing a logical blog every day, progress is definitely visible
4. Learning from peers
What you can learn from your peers is beyond your imagination. Brainstorming. In data work, teamwork is very important.
5. Continuously increase the difficulty of learning
Work with larger datasets. To learn to use spark , you must know that the stopping of learning in the information age is elimination