Academic Advising Resource Center
Data Science
Why take courses in Data Science?
Studying Data Science prepares students to be evidence-based decision-makers, critical consumers of information, and engaged citizens in a 21st Century world that is frequently observed and digitized, constantly evolving, and requires multidisciplinary thinking. Students explore the full data pipeline — including data collection, managing large and small data sets, computational analysis, decision-making, and communicating to broad audiences of relevant stakeholders. Along the way, students develop a toolbox of quantitative and qualitative methods for making sense of complex data, as well as apply data science methods to other disciplines across the liberal arts curriculum.
What kinds of questions does Data Science explore?
- How can we understand real-world phenomena in the natural and social sciences by uncovering patterns in data?
- Where might we benefit from utilizing machine learning to unlock new knowledge?
- What are the social, ethical, and legal implications of algorithmic decision-making?
What advice would you give students interested in taking courses in Data Science?
Exploring data science will help you develop skills in data and information literacy, statistical reasoning, and computational problem solving. DATA 101 is an excellent course to begin with – it provides a gentle (yet rigorous) introduction to the kinds of tools provided by data science and the problems they can be used to solve across many real-world applications. DATA 113 introduces important concepts from statistics that are useful for inferring information from data, such as estimating populations from samples, hypothesis testing, and modeling. CSCI 150 provides an introduction to computational problem solving and programming, enabling work with large-scale data sets and more complex real-world applications. None of these courses assume any prior knowledge about their topics and are appropriate for all students.
Taking Courses
Courses for non-majors or general interest
- DATA 101 and its successor DATA 201 are both excellent courses for all students to gain relevant skills and knowledge in data science that can be applied across any number of other disciplines and real-world applications.
- Because foundational knowledge of statistics is vitally important in many quantitative fields, DATA 113 is also recommended or required for ten other majors, two minors, and four other integrative concentrations at Oberlin.
Getting started in the major
Potential Data Science majors should start with any combination of DATA 101, DATA 113, and/or CSCI 150.
Advanced Placement Credit
Data Science courses do not have any placement exams. Students who have some prior statistics studies (e.g., AP Statistics in high school) or strong mathematical maturity should consider starting with DATA 205 instead of DATA 113; please feel free to discuss with the Data Science chair.
Similarly, students with prior programming experience (including both recursion and object-oriented programming) might start with CSCI 151 instead of CSCI 150; please consult the Computer Science chair for appropriate placement.
Majoring in Data Science
Students majoring in Data Science choose one of three concentrations, guiding their course selection towards different applications of Data Science:
- Natural Sciences Concentration, focusing on applications of Data Science to Biology, Biochemistry and Chemistry, Computer Science, Environmental Science, Geosciences, Neuroscience, or Physics
- Social Sciences Concentration, focusing on applications of Data Science to Economics and Financial Economics, Environmental Studies, Psychology, or Sociology.
- Statistical Theory and Applications, focusing on foundational knowledge in mathematics and statistics for deeper understanding of data science methodologies.
Please refer to the Data Science Major handout for guidance on satisfying the requirements of the Data Science major across the three concentrations.
Provided are three examples of how a student could distribute courses required for the major over three or four years. See the catalog for additional details about major requirements.
Sample Four-Year Plans
| Natural Sciences | Fall | Spring |
|---|---|---|
| Y1 | DATA 101 DATA 113 | CSCI 150 DATA 213 |
| Y2 | DATA 201 CSCI 151 | CSCI 280 |
| Y3 | DATA 373 | CSCI 313 |
| Y4 | DATA 401 CSCI 344 | CSCI 375 |
| Social Sciences | Fall | Spring |
|---|---|---|
| Y1 | DATA 113 | |
| Y2 | DATA 101 PSYC 200 | CSCI 150 PSYC 300 |
| Y3 | DATA 201 CSCI 151 | DATA 213 PSYC 304 |
| Y4 | DATA 373 DATA 401 | PSYC 310 |
| Statistical Theory and Applications | Fall | Spring |
|---|---|---|
| Y1 | ||
| Y2 | DATA 101 DATA 113 | CSCI 150 DATA 213 |
| Y3 | CSCI 151 DATA 201 MATH 231 | DATA 237 MATH 232 |
| Y4 | DATA 373 DATA 401 | DATA 336 |
Related Areas of Study
Biology, Biochemistry/Chemistry, Computer Science, Economics, Environmental Science/Studies, Financial Economics, Geosciences, Mathematics, Neuroscience, Physics, Politics, Psychology, Sociology
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Data Science Major Requirements
Data Science Department