Structure of the Data Science Minor
The minor consists of a total of five courses, three core requirements and two electives. Please keep in mind that some of these courses will also require you to take pre-requisites or co-requisites.
To satisfy the core requirements, a student must choose one course from each of the three categories: Data & Computational Thinking, Data & Statistical Thinking, and Data, Culture & Society.
Data & Computational Thinking
This core requirement will provide you with an introduction to the computing tools and coding methods needed to gather, manipulate, visualize, and analyze data. Taught in Python and/or R.
We are surrounded by computers and devices that share information with each other in a way that seems magical to most of us. After all, computers speak a very different language than ours, one consisting of only two symbols: 0 and 1. So how does your digital assistant understand you when you ask it to play your favorite song? How does it find this song among your entire music collection? How is all your music data stored in your device? And how do computers process the music data to convert it to sound?
To answer all these questions, we first have to learn the language computers use, called a programming language. Programming languages consist of specialized words that express mathematical and logical operations, comparisons, and loops. Getting your device to comprehend the phrase “Play my favorite song” involves transforming your voice command into a set of precise steps made up of only the simpler operations that a computer can interpret. The core requirement Data & Computational Thinking will introduce you to the basics of computer programming — using either Python or R as the programming language. More importantly, this course will teach you how to think about data and problem solving using algorithms, which is our name for the set of logical steps needed to produce an outcome we seek.
Data & Statistical Thinking
This core requirement will provide you with an introduction to data driven statistical analysis, focusing on a hands-on approach to making inferences and predictions to learn from data. Taught in Python and/or R.
Have you ever wondered how your smartphone autocorrects your texts and suggests the next word you are likely to type? The answer is that you and every other smartphone user helped by writing billions and billions of texts over the last decade. The common mistakes we make while texting have trained the programs behind our apps to predict what we may want to type next. Moreover, while some of these autocorrections and autosuggestions are the same for every smartphone user, some are highly personalized. So how exactly does this happen? How are these apps learning and drawing inferences about our texting habits? How is it that the autosuggestions can be so uncannily good?
The complex algorithms inside your smartphone are based on fundamental concepts that are introduced by the Data & Statistical Thinking core requirement. The core requirement covers, among other topics, 1) the role that randomness plays in the way we sample data, 2) techniques to visualize hidden patterns in the data, 3) the inference methods that we have to learn from the data, and 4) the models that allow us to make reliable predictions. All of this is aided by the use of high-powered computers and general purpose programming languages like R and/or Python.
Data, Culture & Society
This core requirement focuses on the social, political, cultural and/or ethical dimensions of data.
Today no concept more directly guides the way we move through the world than that of data. The way we choose to watch a movie, find an article for a research paper, track our workout schedule, like a friend’s post on social media or even get the answer to a question are all fundamentally shaped by the principles of data. Given the centrality of data in our lives, it is important to ask questions about how data impacts human culture, societal formations, and power structures.
For example, how does Netflix know exactly what movie you are in the mood for? What happens to all the data stored in your fitbit? How does Twitter decide what is highest on your news feed? And even more importantly, how is personal data used to allot the number of police in a given precinct? What information is chosen to train machine learning algorithms for facial recognition? How is census information used to draw legislative districts?
Once one begins to think about how central data is to our culture and society, more and more questions abound. The core requirement of Data, Culture and Society within the Data Science minor will address some of these questions, but from the unique discipline in which the course is taught. Each discipline in academia has a slightly different idea about the best way to collect and analyze data to get at the fundamental research questions that motivate it. Therefore, based on your own interests, you will choose one course from the list of options to satisfy the Data, Culture and Society requirement. Each of the courses will teach you about how its specific discipline approaches data and information allowing you to think deeply about what and who counts as data for that field. You will then move into concrete case examples about how data shapes aspects of culture and society such as those ideas that motivate the above questions.
Most departments within the College of Arts & Sciences offer courses that can be used to complete the Data Science Minor. Elective courses focus on the methods and applications of data science or innovative uses of data. The full list of electives as well as a tool to help students choose the best pair of electives to reach their goals can be found here.