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Comment by lutusp | Hacker News Reader

Because for her, programming will be a means to an end, and that end is data analysis and statistics, I recommend that she learn Python and its scientific and analytical libraries like numpy and scipy.

Python is relatively easy to learn, it's free, and it can produce useful results with little effort.

Python: http://www.python.org/

Scipy: http://scipy.org/

Examples of scipy's output: https://www.google.com/search?q=scipy&hl=en&tbo=d...

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While this is true, it seems as though Matlab is the tool used for her work at her laboratory, she needs to join an already going project.

Ah, too bad. Matlab his a steeper learning curve than Python.

yeah, I think it's the major reason for her fearing this field

For what it's worth, if she takes the time to learn how to use these math tools -- and learns the math itself -- she will place herself in a powerful position with respect to future research work, just because so few people bother to acquire a solid grounding in analysis.

I would offer her every encouragement to take the time and absorb the basics of both programming and math. At the moment, a grounding in these topics is essential, and in the future, it will be even more of a necessity than it is now.

If she hasn't done it already or else if she's forgotten it would be a good idea to sit in on a first year linear algebra and or statistics class.

Matlab (and/or numpy / sage / any of the "math" packages) work best performing operations on vectors and matrices of data (long strings of values / grids & cubes (&hyper cubes) of values ).

Expanding on the "little mathematics background" is more important than the "no matlab" issue, one follows from the other.

If she's headed into epidemiology and other such areas then make sure she's read "How To Lie With Statistics" at the very least.

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