Found a good post on Quora.com.

I copied and pasted here so that I can easily find it in the future.

Source: https://www.quora.com/How-can-I-become-a-data-scientist

——- start quote ——-

Strictly speaking, there is no such thing as “data science” (see What is data science? ). See also: Vardi, Science has only two legs: http://portal.acm.org/ft_gateway…

Here are some resources I’ve collected about working with data, I hope you find them useful (note: I’m an undergrad student, this is not an expert opinion in any way).

1) **Learn about matrix factorizations**

- Take the Computational Linear Algebra course (it is sometimes called Applied Linear Algebra or Matrix Computations or Numerical Analysis or Matrix Analysis and it can be either CS or Applied Math course). Matrix decomposition algorithms are fundamental to many data mining applications and are usually underrepresented in a standard “machine learning” curriculum. With TBs of data traditional tools such as Matlab become not suitable for the job, you cannot just run eig() on Big Data. Distributed matrix computation packages such as those included in Apache Mahout [1] are trying to fill this void but you need to understand how the numeric algorithms/LAPACK/BLAS routines [2][3][4][5] work in order to use them properly, adjust for special cases, build your own and scale them up to terabytes of data on a cluster of commodity machines.[6] Usually numerics courses are built upon undergraduate algebra and calculus so you should be good with prerequisites. I’d recommend these resources for self study/reference material:
- See Jack Dongarra : Courses and What are some good resources for learning about numerical analysis?

2) **Learn about distributed computing**

3) **Learn about statistical analysis **

- I’ve found that learning statistics in a particular domain (e.g. Natural Language Processing) is much more enjoyable than taking Stats 101. My personal recommendation is the course by Michael Collins at Columbia (also available on Coursera).
- You can also choose a field where the use of quantitative statistics and causality principles [7] is inevitable, say molecular biology [8], or a fun sub-field such as cancer research [9], or even narrower domain, e.g. genetic analysis of tumor angiogenesis [10] and try answering important questions in that particular field, learning what you need in the process.

4) **Learn about optimization**

5) **Learn about machine learning**

6) **Learn about information retrieval**

7)** Learn about signal detection and estimation**

8)** Master algorithms and data structures**

9) **Practice**

If you do decide to go for a Masters degree:

10) **Study Engineering**

I’d go for CS with a focus on either IR or Machine Learning or a combination of both and take some systems courses along the way. As a “data scientist” you will have to write a ton of code and probably develop distributed algorithms/systems to process massive amounts of data. MS in Statistics will teach you how to do modeling and regression analysis etc, not how to build systems, I think the latter is more urgently needed these days as the old tools become obsolete with the avalanche of data. There is a shortage of engineers who can build a data mining system from the ground up. You can pick up statistics from books and experiments with R (see item 3 above) or take some statistics classes as a part of your CS studies.

Good luck.

[1] http://mahout.apache.org/

[2] http://www.netlib.org/lapack/

[3] http://www.netlib.org/eispack/

[4] http://math.nist.gov/javanumeric…

[5] http://www.netlib.org/scalapack/

[6] http://labs.google.com/papers/ma…

[7] Amazon.com: Causality: Models, Reasoning and Inference (9780521895606): Judea Pearl: Books

[8] Introduction to Biology , MIT 7.012 video lectures

[9] Hanahan & Weinberg, The Hallmarks of Cancer, Next Generation: Page on Wisc

[10] The chaotic organization of tumor-associated vasculature, from The Biology of Cancer: Robert A. Weinberg: 9780815342205: Amazon.com: Books, p. 562