Now that you may have or have not read the following article which we have shared and written in our BLOG many years ago.  Apparently, there may have some correlations why you have chosen to attend a Masters degree program in Business Analytics or to become a future data science professional with the information that we have shared in the past several years.  This article has confirmed AAEA’s early hypothesis, in that the US is experiencing serious shortages for data science professionals.  This is the reason why many higher ed institutions are offering the program, starting in the past a couple of years.

To the current graduate students, the Association has a little suggestion while you are still in your program.  It may help you to take useful classes or are thinking on doing a summer internship before finishing your graduate program.  First and foremost, your classes only fill a-half of the knowledge and expertise in preparing you as the future data scientist.  The other half is coming from the real world.  How can you fill and get the experience from the real world in data science, unless you have been or are employing in the industry or your current company.

If you start fresh the second career as a data scientist, a few things that will increase the odd (with disclaimer) that your dream will come true are:

1. Capitalize the real-world knowledge that you may have had in the past or currently.  For example, if you are, for whatever reason have received your undergrad in “soft” science which have forced you to work at a minimum wage company such as at one of the fast-food chain stores, you can and need to capitalize your past experience in the fast food industry.  Therefore, concentrate to understand what kind of data which you can analyze differently which will help the corporate office of that chain store may see your experience as important core competency.
2. If you never been working, fresh after your undergrad graduation to attend the grad school, you need to think what do you want to specialize in?  For example, after the second and third year, a medical student has a pretty clear idea what he wants to specialize in and where he wants to work and live.  Becoming a brain surgeon, while making the top dollars, may not be fun for those who cannot cope with the work related stress, plus it took extra years to finish the residency program.  If you are unsure, a good start is looking at a grocery store shelf.  The next time when you go shopping at one of your favorite stores, look what are available on the shelf.  This may give your some future ideas what industry or even a company you wanna to specialize in?  Great grocery stores have a wide range of products from the pharmaceutical to fresh produce.  From breakfast cereal to jewelry.
3. Do not avoid hard classes such as advanced econometrics or advanced mathematical statistics.  You got to have those, plus various programming classes where you can learn how to code using Python, R, SQL, C++, JavaScript,  etc.
4. You got to have advanced managerial/microeconomics, managerial finance, and if possible managerial accounting.  Depending on what the super-super specialist that you wanna to be?  Think what a fellow in medical school training meant.
5. You need mathematical programming classes where you will be taught how to model and run a stochastic simulation modeling.
6. If your have a two-year program, try to do a summer internship after finishing two semesters of your first year.
7. Be certified.  Example, SAS offers various options.
8. Write and present manuscripts at various professional annual meetings.  Net working with others in your area of interests.  A data scientist has to know how to write efficient reports – and most likely the readers may not have rigorous background in stat or math.
9. Try to teach undergrad classes–this is good training to be a great presenter and a public speaker.  Data scientists need to know how to present their works to others, which usually non-technical users of your findings.
10. Yes, you need to take 2 semesters qualitative theory, psychometric, survey design and advanced marketing research & consumer behavior courses.
11. Last, but certainly not the least important, work as hard as you can and avoid or minimize taking student loans 🙂