An Introduction to Must Love Data Mondays

Today’s the first Must Love Data Monday.  Since I only started thinking about doing this weekly post last Thursday, I didn’t have time to come up with an idea to research and post. Actually, this weekly post will be another test in my habit of publishing a daily blog post because it’s going to require a little more time and effort.  So as of right now, I’m excited to start writing a weekly post about data & code but we’ll see how much material I can find to write and if I enjoy writing about it.

Today’s post will be more about what is the point of Must Love Data Mondays and why I want to write about data and code.  I guess the first question I should answer is, why do I want to write about data & code?

Why Data & Code?

For the past few years I’ve spent my career working in data analytics.  I’ve been working to turn data into information to be used for my managers, coworkers, and customers.  To get this data and translate it into information I’ve had to learn to code.  Coding is a huge part of my job and it helps tremendously to simplify the burden of organizing and presenting data.  About a year ago, I was thinking about the major themes of my professional career.

  • What was the focus of my career?
  • What should I focus on to continue to advance in my career?

Data & code kept coming up in my life and how I’ve been combining these two themes throughout my entire career. It’s why I’ve continued to learn new languages like Ruby to help me figure out better ways of organizing and distributing data. Lastly, I feel like these two themes will be the key drivers of advancement in my career.

What do I hope to achieve from this series?

Ultimately, I’d like to figure out ways to use data and code to explain & predict certain situations.  I hope it could also be a source of information for people who are interested in data science and how they can find useful online tutorials and classes.  I want to review those course to find ones that are freely available and actually useful.  You want to know if those certificates will actually help you find a job in data science.  Plus, I still want to learn and there is plenty of that to be done.  Also, I’m hoping this will help broaden my experience, which has been mostly in financial data analytics, by researching issues in Pittsburgh, technology,  business, sports, and whatever else I find interesting.

One of the key topics of this series will be data science but what does that really mean?  Here’s a simple definition:

Data Science is the study of generalizable extraction of knowledge from data. [1]

Data Science and Big Data are some of the hottest topics in technology and business.  President Obama has a Chief Data Scientist.  A few years ago, Harvard Business Review said it was, “The Sexiest Job of the 21st Century.” There are certificates offered by Coursera & Udacity. Everyone is talking about data science but I’m not sure how many people actually understand it.  I’m not an expert, far from it, but I’m interested in learning more.

That’s my goal of this blog series.  I’m hoping it helps me to learn more about a topic that has shaped my career.

  1. Dhar, V. (2013). “Data science and prediction”Communications of the ACM 56 (12): 64. doi:10.1145/2500499