Hacker News Reader

Comment by Ftuuky | Hacker News Reader

I'm really interested in learning more about data science. Currently attending moocs to learn R but I don't have much free time and it's a slow progress. Currently I work as analyst for an environmental consulting firm but I always wanted to work in something related to quants. I know I'm probably being naive but I want to change careers. Do you guys recommend that I keep learning R or should I take a closer look to Julia? Or both? I don't have a programming background but I do know statistics and Excel. Edit: fast, concise and useful comments in a matter of minutes, I love HN. Thanks!

View on HN

Become fully confident & proficient in a single programming language before hopping around. If you get to the point where you can build a full application from scratch and answer other peoples' questions on SO or IRC with one, you'll be able to easily get up to speed with another should the need arise.

There are many more resources for learning R, given that the language is older. These resources have become much better in recent years and I think there has been no better time to learn R. Probably the best introduction to data science in R is Hadley Wickham's latest book: http://r4ds.had.co.nz

Learning your second language is much easier than learning your first. You'll be more familiar with programming concepts and terms that make it easier to search for how to do X in that language.

Julia is still relatively new and not quite ready (in my opinion) for new programmers. Things change relatively quickly and I think that could add a lot of confusion that gets in the way of learning.

That being said, I think Julia is a pretty great language. I really love the way it has grown and I'm excited to see how it matures.

Can't answer whether to learn R or Julia, but for a more general education on Data Science, this book might be a really good fit for you:

Data Smart: Using Data Science to Transform Information into Insight

https://www.amazon.com/Data-Smart-Science-Transform-Informat...

All the work is done in Excel. I enjoyed the book quite a bit. Author is Chief Data Scientist for MailChimp.

R is great for producing scientific papers, charts, and doing a vast array of "standard" statistical analyses. I have found python to be much nicer because it's more of a "real" programming language, but the numpy/scipy/pandas stack make it very easy to use for data science and financial analysis also.

There's a (very) quick intro here:

http://lectures.quantecon.org/py/pandas.html

You can also look into quantopian for practice, but I don't recommend actually trading money.

https://www.quantopian.com/

A basic introduction to Julia in Data Science

https://www.coursera.org/learn/julia-programming

>I always wanted to work in something related to quants

Then learn Python, it is mature and has way more study material.

Not to mention Machine Learning libraries like TensorFlow, Scikit Learn & math libraries like Numpy, Pandas, Matplotlib?

Everything there is just set.

I once tried to learn Julia, but the community is just to small to get help at times. It is a work in progress but I recommend you to keep an eye on It.

Conclusion: Think of Julia as a future successor to Python, but not a current replacement.

The programming side is only part of the picture - don't forget to learn some math and theory as well!

Reply on news.ycombinator.com