I  have read several articles on the subject, but none of the authors were really
“Data Scientist” and they admit that, so I thought it was time that something
was written by an actual Data Scientist.

First off, let’s make sure you understand that there’s lots of college
involved, no way around that one. If you noticed a lady in the 2nd row, 3rd from
the left had a mole on her nose in the last commercial you watched, you might
have what it takes, even if you hadn’t thought of Mathematics, Engineering or
Econometrics as a field of study. What I am implying is that it’s take someone
who is VERY observant to be successful in Data Science. Why, because you deal
with such large data sets and large outputs/results, your ability to absorb lots
of information quickly and exacting, is your best friend. I can scrolled a
million records in minutes or run a small SQL script, analyze the results and
tell you if that data is bad or corrupt in minutes. Cleansing data is always the
1st step, if this part if left out, I can guarantee you will have lots of N/A’s
or characters where number should be, etc… so make QA your friend not your

What major or course work produces the best Data Scientist? Econometrics and
Mathematics as long as they have an additional major in Business, why, because
of the logic involved as well as the classic theory of Left Brain people and
numbers. Creative is great for making power point presentations but when you
have 10 terabytes of raw data, pretty is not the 1st things on your mind. Minor
or actively engage in courses that will teach you programming, you don’t need
hard core Pearl but you will need SQL and or Python skills at the very least. Microsoft Visual
Studio, SSAS, SSIS, SSRS package, SAS, SPSS, SQL, Tableau, PowerBI, Visual Basic
are all not only good to know but vital when you have multiple client who use
different CRM, BI and ETL tools.

Once the schooling ends, the real world begins. My 1st boss said, “forget
everything you learned in College, there is no “bell curve” here; meaning,
statistics, programming, mathematics, logics and common sense are only the
start. Practice on cleansing data, extracting data, normalizing data, segmenting
data, loading data, trending data, modeling…. in other words data data data data
data. Never assume your results, never ignore anomalies, do keep a unbiased mind
and never scrimp on tools, software or classes. Yes, that’s right I still attend
webinars and read like crazy to stay sharp on my tools and technic.

We need more people desperately in Science, Technology, Engineering and
Mathematics (STEM) so please consider Data Science as a career. According to the
latest study we’re in high demand and considered rock stars according to

What can your data do you for? @Data_Nerd