Gaining experience with programs like R, SPSS, and Python is an important step in the academic experience of students in the Business Analytics track of the M.S. in Commerce Program. These programs can be daunting for newcomers, yet they are the most common processing programs used in the industry and are important tools for anyone looking to get into data or business analytics roles.
In undergrad, I was always afraid to begin using these platforms. Understanding the syntax, differentiating program types, and moving data across programs are all intimidating ideas that contributed to my hesitancy. Luckily for me, McIntire has a course for Business Analytics track students that allows for students, both experienced with and brand new to these programs, to work with these tools and use real customer data.
The Advanced Quantitative Analysis course provides students to analyze big data sets. Taught by Professor Richard Netemeyer, the course covers discrete choice modeling, classification techniques, data reduction techniques, and advanced predictive techniques.
Professor Netemeyer began the course with a concept that I’ll always remember. He told us that data is powerful. Look at the most successful companies today, like Amazon, Google, and Apple. They all have something in common—they not only collect data, but also utilize that data to make impactful business decisions. Data is useless if you can’t implement the conclusions you can draw from it.
This class has taught me that the companies we all want to work for aren’t really just looking for candidates fluent in code; they want people who are capable of analyzing data on these platforms and communicating that data in a way that helps drive decision making. Data, and working with it in these programs, allows analysts to identify business problems and opportunities and apply that to help come up with a solution.
We also are completing a project in the course where we get to analyze real customer and employee data from a company and mirror what business analysts actually do in the industry, giving us firsthand experience into what we could be doing once we graduate. The project also has few guidelines and lets the data itself direct where the project should go.
Overall, I am thankful for the chance to take the Advanced Quantitative Analytics course here at McIntire and appreciate that it has given me the opportunity to learn and work with programs that I was once hesitant to try. It has helped me to gain confidence and to feel ready for my future in business analytics.
