This course will introduce students to the methods and tools used in data science to obtain insights from data. Students will learn how to analyze data arising from real-world phenomena while mastering critical concepts and skills in computer programming and statistical inference. The course will involve hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. This class is ideal for students looking to increase their digital literacy and expand their use and understanding of computation and data analysis across disciplines. No prior programming or math background is required.
Modes of Thinking Requirement: Thinking Quantitatively and Empirically, Thinking Technologically and Digitally
Credits: 4
Enrollment Cap: 40 students
Adam Poliak
Email: apoliak@barnard.edu
Office hours: TBA
Zoom link:
Hours will be posted here. More information can be found at https://cs.barnard.edu/cs-help-room
The course contains the following components:
By the end of this course, students will:
There are no textbooks required for this course. The course will follow a modified version of the popular online textbook Computational and Inferential Thinking: The Foundations of Data Science. The modified version will be tailored for this specific Barnard course and will be available online. All additional course readings will be made available on Canvas.
The schedule will be broken into the following units:
During an immersive semester, the course will meet 4 times a week and have lab once a week. During a full semester, the course will meet twice a week and have lab once a week. Deadlines and labs below assume a full semester schedule.
| Lecture Number | Topic | Reading | Lab | Homework | Project |
|---|---|---|---|---|---|
| 1 | Introduction | Lab 1: Python & Jupyter Introduction | |||
| 2 | Causality & Experiments | ||||
| 3 | Python Introduction, Data Types | Lab 2: Data Types | |||
| 4 | Tables | HW 1 due Causality and Expressions |
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| 5 | Visualization | Lab 3: Tables | |||
| 6 | Functions & Tables | HW 2 due Arrays and Tables |
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| 7 | Programming Catch up | Lab 4: Functions & Visualizations |
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| 8 | Randomness | ||||
| 9 | Monty Hall & Probabilities | Lab 5: Randomization | HW 3 due Tables and Charts |
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| 10 | Sampling & Empirical Distributions I | Project 1 due (Exploration) |
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| 11 | Sampling & Empirical Distributions II | Lab 6: Sampling | |||
| 12 | Hypothesis Testing | HW 4 due Probability and Sampling |
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| 13 | A/B Testing | Lab 7: Assessing Models / Review | |||
| 14 | Causality | ||||
| 15 | Midterm Review (Catch-up) | Lab catch-up | HW 5 due Hypothesis Testing |
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| 16 | Midterm | ||||
| 17 | Estimation I | Lab 8: Resampling and Bootstrapping |
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| 18 | Estimation II | ||||
| 19 | The Normal Distribution | Project 2 due (Inference) |
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| 20 | Central Limit Theorem | HW 6 due Confidence Intervals & Sample Size |
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| 21 | Prediction I | Lab 9: Correlation, Variance of Sample Means | |||
| 22 | Prediction II | ||||
| 23 | Regression Inference | Lab 10: Regression Analysis | HW 7 due Correlation, Regression, & Least Squares |
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| 24 | Classification I | ||||
| 25 | Classification II | Lab 11: Classification | HW 8 due Regression Inference, Diagnostics, and Classification |
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| 26 | Final Review | Project 3 due (Prediction) |
| Grade Criteria | Grade Scale | ||
|---|---|---|---|
| Percent | Letter Grade | Percent | |
| Participation | 5% | A+ | 97 - |
| Lab | 10% | A | 93 - 96 |
| Weekly HW | 20% | A- | 90 - 92 |
| Projects | 25% | B+ | 87 - 89 |
| Midterm | 15% | B | 83 - 86 |
| Final | 25% | B- | 80 - 82 |
| C+ | 77 - 79 | ||
| C | 73 - 76 | ||
| C- | 70 - 72 | ||
Weekly labs will be graded based on attendance and students will receive full credit by working on the lab assignment until finished or until the end of lab period. Students may opt out of attending the lab in person but must complete and submit it by the end of the week (Friday 11:59pm).
This course is based on the popular Data 8: The Foundations of Data Science at Berkeley. Variations of this course have been taught at:
Cornell University, UIUC, Boise State University, University of Maryland, University of Virginia, and others
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