What advice would you give to undergraduates in science, technology, engineering and math programs to prepare themselves for a job in big data? Do they need an advanced degree?
Undergraduate students do need to continue on to an advanced degree. The good news is you don't need a PhD. We've had great success at the masters level addressing the immediate need in the workplace. My advice to undergraduates is to line-up your coursework with the necessary prerequisites in math, statistics and computer science, to prepare for graduate education. This means going beyond a year of calculus and into linear and matrix algebra. Don't stop with the mandatory course in probability and statistics, which is common with many majors. Take additional courses in areas like multivariate regression and statistical programming.
How can people already in the workforce put themselves in a position to pursue these jobs?
Our model is to pull people out of the workforce for 10 months and immerse them in a rigorous and intensive training program. We turn learning into a full-contact sport and there's nothing like physical proximity to maximize learning. It works, and we've been equally successful with students in their twenties as with those in their fifties. But not everyone can leave the workforce for 10 months. Some can pick-up the necessary skills on the job, in the right work environment where learning is encouraged. Professional certifications -- offered by vendors and professional societies -- can help workers demonstrate their knowledge and advance their career. Online learning may also prove helpful in allowing people to remain in the workplace and upgrade their knowledge. One can even participate in free online learning opportunities.
What skills or training would you consider core to any big data job?
In my mind, big data isn't a new specialty or suite of tools we have to train people into, as much as it's a new organizational reality that everyone will need to adjust to occupationally. How we train marketing people will change. How we train IT people will change. How we train supply chain people will change. And so on. Even how we train executives will change. Everyone across the board needs more formal training in statistical analysis, and it should start early in the education process. It would be valuable to develop interdisciplinary curricula around the emerging concept of "data science" as a way of blending elements of math and statistics and computer science.
Looking across the organization, some occupational roles will require additional computer and statistical programming skills, other roles will require new data management and data cleaning skills, and yet other roles will require skills in data visualization and interpretation.
Advanced degree programs are being created rapidly. What type of skills can someone gain from your program?
What's unique about our Master of Science in Analytics is that we started from scratch to build a fully integrated learning experience from end-to-end, and we positioned employers as our customer. Our goal was to directly address the employer need, in terms of the kind of talent they sought to hire. Technical skills are only one part of the package. Employers want people who understand the methods and applications of analytics, but also who are focused on the business problem (not the data alone), able to work in multi-functional teams, and who can effectively communicate insights to executives.
Our homegrown algorithm for creating an analytics professional -- both in terms of the content and structure of the program -- balances technical and tool skills with teamwork and communication skills. It's been a potent formula for us that yields phenomenal results in terms of student outcomes in just 10 months.