In previous InFocus posts, I’ve discussed how Business Intelligence professionals and STEM graduates can make the move toward becoming Data Scientists. Now I’d like to discuss those people that would fit in the upper left quadrant of the skills matrix (Quadrant “C” below)– how business analysts and quantitative analysts can take steps to become Data Scientists.
In the Data Science and Big Data Analytics classes we’ve held so far, I would say those with the skills/ability mix of this quadrant tend to be Quantitative Analysts, Statisticians, Business Analysts, and Data Analysts. This group generally has some background in quantitative methods, whether it is statistics, mathematical finance, or other training in quantitative methods. They also have some experience with technology, although this may be more limited.
From my experience, this group generally embraces data science and the teachings from the course. I believe this is because they are in roles that require them to solve various kinds of data problems, although they may not always have the experience or training in advanced analytical methods to solve these problems.
EMC’s Data Science and Big Data Analytics course introduces them to more advanced analytical methods and technologies, which enable them to make leaps forward. For instance, people who are pricing analysts may have the responsibility of analyzing discount rates for products, creating dashboards, and looking at trends of pricing degradation over time. One business analyst who completed the course really took to these new methods from machine learning and statistics, and now has significantly raised the level of his analytics. With this new training, he is able to use different kinds of regression models to predict pricing for products and product sets, and can determine optimal pricing for specific situations. He can use a much more rigorous approach to establish market pricing, thus increasing his value as an analyst.
Ellis Kriesberg, Manager of Business Operations at EMC, shared his perspective with me after completing the Data Science course, “The course helped me in three key ways. First, it helped me better understand the different data science techniques, and which are best for particular problems. Second, it made me realize that my interest in applying predictive analytics is shared by many others at EMC and is part of a growing movement. Finally, it provided a very useful methodology for summarizing and presenting the results of data science projects.” Ellis is a great example of someone who already had strong analytical skills, and was able to leverage his newly acquired data science techniques to build his expertise and apply these methods to his job immediately.
Other people have become more proficient at data cleansing, enabling them to now leverage more data, and “messier” data, than before, as well as use some of the power of open source tools such as R or Postgres SQL to manipulate the data and prepare it for a more rigorous analysis than they may have done in the past. In general, the profile of a person in Quadrant C is someone used to doing analysis, but who may not have had the tools in their tool set to fully solve some of the problems they are encountering. Once we give them more tools, I find they readily try them out and are hungry for more.
Some of the people in this quadrant have become strong advocates for the Data Science course, and evangelists of data science and machine learning within their departments. They are enthusiastic about trying out the new methods they learned, and have ample projects on which to try them. This becomes a virtuous, reinforcing cycle – they try new methods on problems, they learn from the experience, and they build on their skills when they move onto the next problem set.
For business analysts in this quadrant, learning just a few new methods and a bit of technology enables them to be much more proficient with the day-to-day problem sets that they face. I recommend that people in this quadrant try some light programming or a few hands-on introductions to analytics to further their skills. Nathan Yau’s book “Visualize This” is a good introduction to visualization and storytelling using open source tools, and can serve as a gentle introduction to improving how to portray data, as well as to programming in Python, R, and other languages. Similarly, “The Art of R Programming” by Norman Matloff provides many hands-on examples for using the R software and programming language to solve analytical problems.
Ellis also suggested the following to continue learning:
- Look at some of the free on-line courses on machine learning or data science, such as “Computing for Data Analysis”
- Join a Meet Up group, such as Boston Predictive-Analytics (in my area, though these are all over the world now).
- Use free online text books and articles about data science such as “Introduction to Statistical Thought” by Michael Lavine, which makes extensive use of R
In my next InFocus post, I’ll discuss the people in Quadrant “D” of the Skills Matrix, those with strong quantitative and technical backgrounds.