Q: What skills or attributes do students need to thrive in an analytics program?
For students to thrive in an analytics program, they need to have a number of attributes and skills. The most important is that they be really smart. And clearly, they need to have an aptitude with numbers and numerical things. But let’s step back and think about what analytics is. In particular, business analytics is a combination of predictive modeling to solve business problems. So the best students are the ones that have some background in business and some background from a quantitative science.
Q: Where’s the limit to what students can learn in the classroom? What do they have to learn on the job?
Students learn a lot in the classroom. They learn technical skills, they learn soft skills, teamwork skills, presentation skills. But for them to be effective, they need to learn how to apply this in their job. And one of the great things about the MS Analytics program at Texas A&M is there is a work-based project that runs across the whole five semesters of the program. So right from the boot camp, week zero, students are thinking about how they can apply what they’ve learned in the classroom to their work situation.
Q: Which fields do you think are due for the biggest ‘shake up’ as the use of analytics becomes more prevalent?
Analytics is set to shake up a lot of fields and areas. If we just take areas that are represented by students currently in our program, oil and gas and healthcare are areas where predictive modeling is going to have a huge impact. Lots of organizations use score cards where what you do is you can tell where you are today or where you were yesterday. Predictive modeling can predict the future and hence predict are we going to have a problem, do we have an opportunity? And that will lead to a big shake up.
Q: It has been said, “there are three kinds of lies: lies, damned lies, and statistics.” With this in mind, what are some best practices for interpreting data when making business decisions?
Some of the key attributes of being effective using statistics in business, one is to be able to fit a predictive model to predict the future. And the other is to produce a graphical summary of the results that’s easily understood by management, by folks at the board, even by our customers. So in order for people to be effective, they need to understand things at quite a technical level, but they also need to be able to communicate them to a wide cross-section of folks.
Q: As a professor, what advice would you offer students unsure if a Master’s program in Analytics is right for them?
My advice to prospective master’s students goes something like this. The most important thing is to keep your options open. You want to find a career that you can grow into in a field that is likely to expand. In terms of analytics programs in particular, you want to ask yourself, are you trying to change your career or are you trying to enhance your career with your current employer? If you’re trying to enhance your current career, then you should consider a program where what you learn can be applied directly to your job.
Q: What should students look for when deciding on a Master’s program in Analytics?
There is an ever increasing number of analytics programs. I’ll give you the following advice about comparing these programs. First, look at the reputation of the school, the quality of the professors, but also importantly, look at the quality of the peer group, the folks with whom you’ll study. It’s important that the programs offer both rigor and relevance. Rigor in the sense that they focus on the latest tools and techniques, and relevance in the sense that the skills that you learn can be directly applied to the data collected by your organization.
Q: Final Thoughts.
We live in the big data age. Companies spend a lot of time and effort collecting and combining data. The time is now right for actually mining the data and coming up with models that can predict the future. And the best models include both business acumen – what are the likely most important predictors – and also the latest methodology so that the model is both effective and efficient.