CSCI 1090 Data Science Principles
This course will provide students with an overview of the field of data science and its responsible uses, along with an introduction to programming in Python from a data science perspective. An emphasis will be placed on solving problems and applying data science principles to real-world datasets. Topics will include variables, data structures, control structures, functions, exploratory data analysis, data manipulation, and data visualization, as well as an introduction to descriptive statistics and machine learning. Students will engage through readings and in class discussions on topics such as applications of data science for the common good, privacy in a digitally connected world, issues of representation and omission in data collection, biases inherent in constructing information infrastructures and classification schemes, and the impact of algorithmic decision-making.
Course overview
- Department
- Computer Science
- School
- MCAS
- Credits
- 3
- Level
- Undergraduate
- Offered
- Periodically
Requirements fulfilled
- Neuroscience B.S.: Computation (6 Credits) — Any one of the following courses (Current University Catalog; students should confirm their catalog year)
- Psychology B.S. (Class of 2027 and Before): Computational Corequisite — two of the following courses (6 or more credits) (Class of 2027 and Before)
- Psychology B.S.: Computational Corequisite — two of the following courses (6 or more credits) (Class of 2028 and After)
Course evaluations
- Course rating
- 3.85 / 5 · Very Good
- Instructor rating
- 3.96 / 5 · Very Good
- Terms evaluated
- 6
| Term | Instructor | Course overall | Instructor overall | Well organized | Clear learning objectives | Intellectually challenging | Helpful assignments | Applicable beyond course | Attendance necessary | Weekly effort | Responses |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Spring 2026 | Cristina Maier | 3.7 | 3.8 | 4.3 | 4.3 | 4.1 | 4.4 | 4.3 | 4.1 | 1–3 hrs/week | 63 of 71 |
| Fall 2025 | Jessica Finocchiaro | 4.1 | 4.4 | 4.2 | 4.4 | 4.3 | 4.6 | 4.6 | 3.8 | 4–6 hrs/week | 43 of 48 |
| Spring 2025 | Cristina Maier | 3.3 | 3.2 | 3.7 | 4.2 | 4.3 | 4.3 | 4.2 | 4.0 | 1–3 hrs/week | 24 of 31 |
| Fall 2024 | Emily Prud'hommeaux | 4.2 | 4.3 | 4.3 | 4.5 | 4.3 | 4.6 | 4.5 | 4.4 | 4–6 hrs/week | 23 of 29 |
| Spring 2024 | Emily Prud'hommeaux | 4.2 | 4.1 | 4.3 | 4.4 | 4.2 | 4.5 | 4.5 | 4.0 | 1–3 hrs/week | 38 of 40 |
| Fall 2023 | George Mohler | — | — | — | — | — | — | — | — | — | 34 of 38 |
Data freshness
Instructors
Sections
- Section 01
- Section 02
- Section 01
- Section 02
- Section 03