We are living in an increasingly data-driven world, with data literacy a highly sought-after skill by employers. At St. Olaf College, the Introduction to Data Science course provides students from all disciplines the opportunity to learn data analysis and visualization.
Taught within the Mathematics, Statistics, and Computer Science (MSCS) Department, this introductory course teaches students the basics of data science, an emerging field that seeks to gain information and make decisions based on collected data. By learning to interpret data and make it visually accessible for others, students understand the importance of data and its applicability to everyday issues.
At its heart, data science is about gleaning information and making decisions from data. Knowledge of how to organize, visualize, and analyze data has never been a more important or marketable skill.Professor of Statistics and Data Science Paul Roback ’89
“We want to empower students to use real, messy data to answer interesting questions by finding and scraping data, tidying and transforming the data, and ultimately creating visualizations that tell a compelling story,” says Kenneth O. Bjork Distinguished Professor of Statistics and Data Science Paul Roback ’89. “At its heart, data science is about gleaning information and making decisions from data. Knowledge of how to organize, visualize, and analyze data has never been a more important or marketable skill.”
Since Introduction to Data Science is one of the requirements for the Statistics and Data Science concentration, many students interested in pursuing careers in data science take the course during their time at St. Olaf. However, the class is ultimately designed to give any student a basic understanding of data visualization, and can complement many majors. In fact, the course has led many students outside of the MSCS department to work with data after graduation. For psychology major Emily Patterson ’20, the course was instrumental in shaping her career interests.
“This class informed my decision to focus on data science rather than psychology as my career. I always liked statistics and math, but did not see myself majoring in either of those subjects. I wanted to concentrate in statistics to improve my chances of getting a psychology job, but I really enjoyed this course and it encouraged me to continue taking higher-level statistics classes at St. Olaf,” Patterson says. “It made me realize I could see myself doing this work full time.”
Patterson now works as an associate data scientist in Downers Grove, Illinois. Although she did not have experience with data science prior to taking the course, Patterson quickly caught on to the material and received the support she needed to succeed.
Emily Patterson ’20This class informed my decision to focus on data science rather than psychology as my career. I always liked statistics and math, but did not see myself majoring in either of those subjects. I wanted to concentrate in statistics to improve my chances of getting a psychology job, but I really enjoyed this course and it encouraged me to continue taking higher-level statistics classes at St. Olaf.
“The class encouraged working collaboratively, and throughout the classes we would walk through code together, which was very helpful to my understanding of the material. There was a lot of code from class available to help with assignments and to reference throughout,” Patterson says. “In both Intro to Data Science and Statistics for Science, there is a learning curve when being introduced to R, a programming language, but the professors are always available during class and office hours to help you along.”
Megan Backhaus ’19, a biology major and statistics concentrator who now works as a data scientist with UnitedHealth Group, also found the class highly accessible and foundational for students outside of the MSCS department.
“I found the Intro to Data Science class to really help with my coding skills. I became a proficient coder after this course. The thing I love about all the statistics and data science courses offered at St. Olaf is that they are so applicable to everyday practice. I use the code packages taught in this class every day at work,” Backhaus says. “I feel really lucky that I created my data science foundation in this class. Professor Roback touches on a lot of important aspects of data science all in one semester! By taking this course, I was prepared for my first day on the job.”
In addition to teaching students the foundations of data science, the course also allows them to create their own project using existing data. By the end of the course, each student publishes a web page that tells a data-driven story about a topic of their choice. The websites, built with coding programs like Shiny Apps or RPubs, often utilize interactive graphics that aid viewers in understanding the information. Past projects have explored data on topics including NASA findings on stars and constellations, the activity of squirrels in New York, and patterns of dialogue in Star Wars movies.
Megan Backhaus ’19I feel really lucky that I created my data science foundation in this class. Professor Roback touches on a lot of important aspects of data science all in one semester! By taking this course, I was prepared for my first day on the job.
“This was a really cool project,” Backhaus says. “I actually create Shiny Apps frequently to share results at work. Shiny Apps are a great way to share visualizations with others, giving the viewer a lot of power to decide what they want to look at.”
For her web page, Backhaus visualized breweries across the United States, mapping them by latitude and longitude so viewers could identify breweries in their area. Patterson’s web page focused on environmental conditions and their effect on physical health. These projects allowed the students to wrangle data in ways that are applicable to our current world and develop skills that are aiding them beyond their time at St. Olaf.
“The foundational skills I learned in this class help me every day while I create interactive dashboards for business stakeholders to better visualize findings so they can make decisions,” Backhaus says. “Without this class, I don’t think I would have found a career in data science!”
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