Some questions you need to answer for being the Data Scientists

1. Learn to love data

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.

-> 실질적인 프로젝트에서 이론을 배워가는게 이론을 정독하는 거 보다 기억하기도 쉽고 지속적으로 공부할 수 있는 것같다! 

    대신증권 API 불러와서 주식 데이터를 가지고 패턴 예측하는 걸 남은 시간에 해봐야지 ! 

Not everyone is obsessed with predicting the stock market, I know. But it’s important to find that thing that make you want to learn. 

-> 작년부터 관심있었던 개인 맞춤형 여행 경로 추천 시스템은 항상 만들어 보고 싶었던거!! 어떤데이터가 필요한지 고민해봐야겠다.

2. Learn data science by doing

Knowing a few algorithms really well is better than knowing a little about many algorithms.

• If you know linear regression, k-means clustering, and logistic regression well, can explain and interpret their results, and can actually complete a project from start to finish with them, you’ll be much more employable than if you know every single algorithm, but can’t use them

Another technique (and my technique) was to find a deep problem, predicting the stock market, that could be broken down into small steps. I first connected to the yahoo finance API, and pulled down daily price data. I then created some indicators, like average price over the past few days, and used them to predict the future (no real algorithms here, just technical analysis). This didn’t work so well, so I learned some statistics, and then used linear regression. Then I connected to another API, scraped minute by minute data, and stored it in a SQL database. And so on, until the algorithm worked well.

-> Use algorithms as a tool for improving the predict accuracy. Not just studying the algorithms 

Learning without application isn’t going to be retained very well, and won’t prepare you to do actual data science work.

3. Learn to communicate insights

• 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.
• Try to speak at meetups.
• Use github to host all your analysis.
• Get active on communities like Quora, DataTau, and the machine learning subreddit.

http://blog.udacity.com/2015/04/data-science-interview-questions.html