Lots of data? QUEST students can handle it.
After a lot of thought and hard work, QUEST’s Applied Quantitative Analysis course (BMGT438A/ENES438A) really came to life this semester! In the tradition of QUEST having “a track record of experimentation and innovation in the classroom, the data course is no different,” according to Executive Director Dr. Joe Bailey. Since former Director Kylie King saw an opportunity for QUEST to do something innovative with data a few years back, QUEST has been developing a data class as a way to prepare its students for the analytical work to be done during 490H and post-graduation. Co-taught by Dr. Joe Bailey, Professor David Ashley, and QUEST alumnus Josh Kohn (Q18), the class features each professor bringing the best of their knowledge in data analytics; they rotate weekly to teach the students everything from running linear regressions to understanding the data behind artificial intelligence. According to Professor Ashley, this course is a “good opportunity to showcase data management [and] authenticate other statistics classes.”
Each week, students learn vast amounts of information and techniques surrounding statistical analyses. However, this class is unique in that the majority of it is application-based. Professor Ashley favors a hands-on approach so that students possess a “skill set to recognize when to use each method and the meaning it provides.” He wants to see storylines behind the data sets to understand why they are important rather than equations lacking context.
Brianna Ho from Q29 says her favorite part of the class is “how wide the breadth is and how we can learn so much in such a short amount of time.” She said, “We’re able to learn things that are applicable to different classes like how to use Excel and also more conceptual topics like Josh’s lecture on machine learning and AI.” Although there is so much to learn and we cannot become expert data scientists in one semester, Josh Kohn says a goal he has for the students is “to learn the key topics and buzzwords so [they] can dive deeper if [they] want to… and be able to have an intelligent conversation.”
The students are not only getting to apply what they learn to in-class exercises, but they are also working on some great projects with real clients. This course has gone “from a 1-credit offering to what it is today — a 3-credit course with approximately 40 students all doing experiential projects,” says Dr. Bailey. All of the students are formulated into groups working for Leidos, Unilever, Google, the US Office of Personnel Management, the Boys and Girls Club, and the US Patent and Trademark Office. Charles Grody from Q29 finds that what he most loves is that “there is so much to explore” and is challenged by deciding when to “go in depth to answer one question or when to try and answer several.”
To highlight some of the projects, the Google team entered the company’s Kaggle competition which involves producing the best model for describing the data set. This year, the premise of the competition involves figuring out how much an average customer spends at the Google store. The group for the US Patent and Trademark Office is looking at data to find interesting trends regarding patent application conversion rates in order to better understand the variables that predict whether or not a patent application will be accepted. The Unilever team is studying datasets from the biggest ice cream factory in the world to better understand aspects impacting waste factor in an effort to reduce it without sacrificing a high level of service. The Leidos team is examining artificial intelligence tools that can be used to improve pilot workflows. The Boys and Girls Club group’s goal is to analyze donorship data, from understanding what categories are doing the best to what trends can be seen amongst the donors.
Anna Xi (Q29) feels like the most important skill she has learned so far is that “dealing with large amounts of data means finding strategies to consolidate the data into different categories.” Students ultimately receive raw data and convert them into stories by sorting through and cleaning up the data. These stories provide an understanding that allows inferences to be drawn and relayed to the clients. Josh called this course “very QUEST-y” as students are “blending technology with business context.”
The QUEST student experience culminates in the capstone course, 490H, and as time has gone on, the projects have become more challenging, especially with the data involved. The Applied Quantitative Analysis course gives students a huge advantage in order to create a more level playing field. Data analysis will no longer be burdensome for students as they work through their projects. Kohn believes that “once it clicks, [students] will be amazed at the variety of problems [they] can analyze,” and it’s this moment in the course that is Dr. Bailey’s favorite as he says, “It is at that point that the course material isn’t just surface-level learning.” Furthermore, this course is very applicable to many QUEST students’ future goals as the ability to apply the methods learned goes a long way beyond QUEST and into many careers.
Josh Kohn said, “The world is becoming increasingly data-driven and generating more data points than companies or governments know what to do with. You can apply these skills to any industry you choose and understanding statistics pays many dividends.” In conjunction with that, Dr. Bailey sees that “every major within QUEST is shaped by the move towards analytics.” All in all, speaking from personal experience, the Applied Qualitative Analysis course has certainly been challenging, but I have learned so much and have simultaneously gotten to see all my knowledge being put to use.