My learning as a Data Scientist

So apparently the new en-vogue title is Data Scientist.  I can now include that to my already expanding list of titles.  In the past I've been known as an Engineer, Operations Analyst, Production Control Specialist, and an Analytics Analyst.  Now I'm considered a Data Scientist.  It's all the same to me.  My training and expertise has allowed me to problem solve many challenges within organizations.  The title doesn't matter.  There are opportunities for people with my skill set.

A recent blog post by Kontagent Kaleidoscope about Big Data is Useless without Science got me thinking about my role as a self-proclaimed Data Scientist.  The blog article points out a need for the science of better decision making.  Organizations are looking for people to help them turn their data mines into information gold.  I've definitely learned a lot over the years as a Data Scientist and I thought I would list some of those learnings.

1.  Organizations Don't Know What a Data Scientist Can Do

The idea here is marketing your own talents.  The Data Scientist needs to put their methods and work out there for the organization to see and touch.  This means working with the peers and management in the organization.  The Data Scientist needs to be able to eloquently relate methods, problems, challenges and how they can be solved.  Important skills here are personal marketing and communication.  I know this goes against the grain of many numbers geeks like me.

2.  Problems Don't Solve Themselves

Opportunities for solving real problems in an organization are always around.  The trick is being in the right place at the right time to be able to solve those problems.  Organizations have hoarded a lot of data and many times they don't even remember why.  The Data Scientist needs to turn into a Data Detective.  Explore all aspects of the organization.  Interview different departments and see how they tick and ask questions like "What keeps you up at night about your job?".  I was often surprised how a simple solution would go a long way to helping someone else out.  This develops true collaboration and leads to bigger problems to solve.

3.  Always Continue to Learn New Things

The world is constantly evolving and there are always new tools, tricks, methods, algorithms, software and mechanisms.  The Data Scientist needs to be able to adapt to new technologies.  I've found its best to stay current with whats new in order to stay sharp and meet new demands.  The internet can be your friend.  Even keeping up with a favorite list of blogs can help with staying current.  Times change and so do organization's needs.  Perhaps this is just me but I love learning new things as it creates a fun diversion and improves my skill sets.

Day in the life of a Data Scientist

A great read from the Decomposition blog about the day in the life of a Data Scientist.  I consider myself a Data Scientist by any other name.  The blog article by Sean does a great job of breaking down the essence of making better decisions for the organization you may be involved.

I've always thought asking good questions is the start of good analysis.  The organizations basically doesn't know what it doesn't know.  A good Data Scientist will be a like a sleuth looking for clues.  In all honesty that may be the most fun about being a Data Scientist.

Data Mining Books List

I came across a great list of Data Mining Books while perusing around the internet.  The list is maintained by Kurt Thearling who is Director and Chief Scientist in various organizations helping to develop their Analytics and Engineering groups.  Kurt has written some white papers on the subject of Data Mining and has also been featured on NPR.  Kurt's NPR piece was about data mining and privacy which is obviously a big subject in our Facebook society today.

I believe this is probably one the the most comprehensive lists of Data Mining books available.  If you are interested in obtaining one of these books please be sure to peruse the the new IEOR Tools Online Store Data Mining section.  There you can find books and references on Data Mining with varying levels from introductory to advanced applications.