I’ve been learning more about data science in the last couple of months and recently stumbled upon a very good blog post from Dataquest on how to learn data science.
First, it’s important that there is some inherent motivation to learn data science:
Nobody ever talks about motivation in learning. Data science is a broad and fuzzy field, which makes it hard to learn. Really hard. Without motivation, you’ll end up stopping halfway through and believing you can’t do it, when the fault isn’t with you – it’s with the teaching.
You need something that will motivate you to keep learning, even when it’s midnight, formulas are starting to look blurry, and you’re wondering if this will be the night that neural networks finally make sense.
You need something that will make you find the linkages between statistics, linear algebra, and neural networks. Something that will prevent you from struggling with the “what do I learn next?” question.
My entry point to data science was predicting the stock market, although I didn’t know it at the time. Some of the first programs I coded to predict the stock market involved almost no statistics. But I knew they weren’t performing well, so I worked day and night to make them better.
There are good links throughout, including 100 data sets for statistics.
I like the suggestions on communicating your findings and/or your learning process:
Part of communicating insights is understanding the topic and theory well. Another part is understanding how to clearly organize your results. The final piece is being able to explain your analysis clearly.
It’s hard to get good at communicating complex concepts effectively, but here are some things you should try:
Start a blog. Post the results of your data analysis.
Try to teach your less tech-savvy friends and family about data science concepts. It’s amazing how much teaching can help you understand concepts…
More resources and links here.