The “Data Scientist” explained: more than just a buzzword

We live in a brave new world where people possess far more data than ever before in history, but the amount of information we have per unit of data has never been lower. As people struggle to make sense of all this data, several new terms emerged, such as: Big Data, Business Intelligence, Map-Reduce, and Data Scientist… but what do these new words mean?

Definitions for these terms continue to emerge, but I’d like to share what I’ve learned about the “Data Scientist”.

I was inspired to write this because of an email I just got from Kaggle. (For those of you who don’t know, Kaggle is a website that offers analytical challenges. These challenges are open to anyone, and the best answer wins prize money that ranges from hundreds to millions of dollars.)

Kaggle’s Anthony Goldbloom offers this self promotional but awesome tidbit that helps explain the role of the data scientist:

Thus who you decide to hire as your first data scientist — a domain expert or a machine learner — might be as simple as this: could you currently prepare your data for a Kaggle competition? If so, then hire a machine learner. If not, hire a data scientist who has the domain expertise and the data hacking skills to get you there.

Recently, I was reading about Map-Reduce, and I came across another nice explanation of the data scientist. This explanation is more comprehensive, yet still concise.

Data scientists use a combination of their business and technical skills to investigate big data looking for ways to improve current business analytics and predictive analytical models, and also for possible new business opportunities. One of the biggest differences between a data scientist and a business intelligence (BI) user – such as a business analyst – is that a data scientist investigates and looks for new possibilities, while a BI user analyzes existing business situations and operations.

Data scientists require a wide range of skills:

  • Business domain expertise and strong analytical skills
  • Creativity and good communications
  • Knowledgeable in statistics, machine learning and data visualization
  • Able to develop data analysis solutions using modeling/analysis methods and languages such as MapReduce, R, SAS, etc.
  • Adept at data engineering, including discovering and mashing/blending large amounts of data

People with this wide range of skills are rare, and this explains why data scientists are in short supply. In most organizations, rather than looking for individuals with all of these capabilities, it will be necessary instead to build a team of people that collectively has these skills.

Map Reduce and the Data Scientist, by Colin White (January 2012)

Granted, there’s an element of self-promotion here too, but this is a great description. I’ve had a hard time explaining my professional value proposition when I meet new people, because there are so many new concepts involved in my areas of specialization, and this description is quite helpful.

As companies are recognizing their need for someone to fill this role of the data scientist, they’re clearly struggling to define the role, advertize for the position, and evaluate candidates. Often they are overly focused on technical requirements, and they’re seeking a PhD in machine learning, or someone with years of database programming experience.

It seems to me that they usually need someone who understands concepts like cross validation, or decision trees, and knows more than the difference between a flat file and a relational database, but the most important thing is that they need someone who can understand business problems, communicate to business leaders, and appreciate the technical considerations for application development.

Better link and explanation here

Update: Sunshine in Chicago compared to Anchorage and Miami

I updated the last post to include a link to the source code, and updated the plots with attribution to the data source,

Based on Jim’s comment on the last post I thought it would be easy to re-run the analysis for Anchorage.  However, the Anchorage data was more difficult to handle, due to a period of continuous twilight at various times in the year.

So, as a workaround I just downloaded the tables for sunrise and sunset.   Personally,  I was more curious about Miami than Anchorage… but they are both easy to run with the new code.

Here’s what we gain / give up in terms of daylight for these locations.

Also, I thought that the speed at which the days change was much more interesting when comparing cities:

The way that the website deals with a

Sunshine in Chicago

My favorite day of the year is December 21, because that’s the day where the days finally start getting longer.

I’ve always wondered how quickly we gain and lose time as the seasons change, and so I thought I would try “scraping” the data off the web.  Here is that result:

Although it’s interesting to note to see where the days are getting shorter and longer, something else grabbed my attention along the way to this graph.  I was interested by the effect of daylight savings on our day.

In my younger days I loved that magical weekend when we “gained an hour”, because it felt easier to wake up for at least one Monday a year.  These days I feel much more anticipation for the spring ahead weekend, when we regain our fair share of sunshine.

Here’s what our days look like currently (with daylight savings):

Here is what our days would look like without daylight savings:

Source code:

Installing StatET

EDIT: Completely ignore the advice below. R Studio is now the way to go for an R development environment. It was a viable alternative about a year after I wrote this post, and now it’s hands down the only way to go.

About StatET and Eclipse

StatET is a powerful plug-in that allows you to use R inside the Integrated Development Environment (IDE) known as Eclipse. The features in Eclipse make it easier to write code in R, unless perhaps you’re already using something more sophisticated.

Eclipse has a reputation for having a “steep learning curve”. However, I have found it to be useful even if you barely know what you’re doing. The more you learn, the more useful it becomes.

StatET has a reputation for being difficult to install. There are a few things that tricky for non-programmers. Hopefully this post will make those things more obvious.

StatET is written by Stephan Wahlbrink. The official website and more detailed instructions can be found here:

System Requirements

I will be showing you how I installed the plug-in for Eclipse Indigo, using R 2.14.1. I’m using a Windows XP machine. The process is similar for Windows 7.

My Steps
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