Transitioning from Nonprofit management to Data Science?

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SUMMARY

The discussion centers on transitioning from nonprofit management to a career in data science. The individual has a background in mathematics and has recently completed MIT's Computer Science 6.00x course, along with enrolling in a Machine Learning course on Coursera. Recommendations include pursuing a master's degree in Computer Science (C.S.), Electrical Engineering (E.E.), or Statistics, focusing on programs that emphasize pattern recognition and machine learning. Researching various departments and their offerings is crucial for finding the right fit for advanced studies in data science.

PREREQUISITES
  • Understanding of basic programming concepts and languages, particularly Python.
  • Familiarity with statistical analysis and data interpretation.
  • Knowledge of machine learning principles and algorithms.
  • Experience with data manipulation and visualization tools.
NEXT STEPS
  • Research master's programs in Computer Science, Electrical Engineering, and Statistics that focus on data science.
  • Explore advanced machine learning techniques and their applications in real-world scenarios.
  • Learn about data visualization tools such as Tableau or Power BI.
  • Engage in projects that utilize Monte Carlo simulations and other statistical methods to solve data-related problems.
USEFUL FOR

Individuals looking to transition into data science, particularly those with a background in mathematics or related fields, as well as professionals seeking to enhance their skills in programming and statistical analysis.

ijl
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Hi all,

I am trying to figure out the best way to transition into a new career.

I obtained my undergraduate degree in mathematics in 2010. Up until the beginning of my senior year I was determined to go on to do a PhD in pure mathematics. However, I started second guessing myself when I saw a very talented student who was 2 years ahead of me drop out of his PhD program.

My motivation for going to graduate school was pretty superficial. I thought it was just what a pure math major was supposed to do. So instead of applying to grad school I signed up for a graduate level algebra course as a non-degree seeking student the following semester and began interning for a small non-profit organization. I passed the course with a B, but really didn't see a math PhD as my calling.

I enjoyed my internship, so I took a job as a manager with the non-profit instead of grad school.Currently, my job consists of writing proposals for grant funding, creating marketing materials, and managing donor/donation databases.However, I've reached a point in my career where I do not think I can gain much more from my current job. My school debts are completely paid off now and I am ready for a new challenge.

I just finished taking the first offering of Computer Science 6.00x through MIT and EDX. I loved the course. I was especially fond of programming Monte Carlo simulations to solve problems related to epidemiology. I am now signed up for a coursera course on Machine Learning through Stanford.

Data science really seems like a career I would like to pursue. Does anyone have any reccomendations for how someone of my background could enter this field? Is there a master's level program that would prepare me for this type of work?
 
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When you say "data science" I assume you mean things like pattern recognition, machine learning and in general ways of developing algorithms to find patterns in large data sets?

If so, this is a well-established field you can study at the graduate level in C.S. or E.E. departments. I can't speak for all departments because I don't really know what defines C.S. or E.E., but these are at least pretty classic topics in C.S. There may also be overlap here with statistics. "Data science" tends to be a somewhat catch-all interdisciplinary term.

In any case, with a math B.S. and the high level of interest you clearly possess, completing an M.S. in this area should be no problem. The only tangible advice I can offer based on what you said is to do some research on various departments (CS, EE, stat, possibly others), to see what kind of research they have going on, and what courses they offer, what the professors are experts on, etc. Just research department websites as if you were a PhD applicant, so you can be sure the program has what you are looking for.
 

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