Introduction to Computational Thinking and Data Science

Catalog Description

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

Course Staff


Adam Poliak
Office hours: TBA
Zoom link:

Teaching Assistant: TBA

Barnard CS Help Room:

Hours will be posted here. More information can be found at

Course Components:

The course contains the following components:

Learning Outcomes:

By the end of this course, students will:

Course Materials

Textbooks Required:

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.

Course Overview

Course Topics:

  1. Python Basics: Using Python to manipulate information
  2. Visualization: Interpreting and exploring data through visualizations
  3. Probability: Making assumptions and exploring their consequences
  4. Sampling: Understanding the behavior of random selection
  5. Inference: Reasoning about populations by computing over samples
  6. Prediction: Making predictions from data

Schedule (Tentative)

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
5 Visualization Lab 3: Tables
6 Functions & Tables HW 2 due
Arrays and Tables
7 Programming Catch up Lab 4:
Functions & Visualizations
8 Randomness
9 Monty Hall & Probabilities Lab 5: Randomization HW 3 due
Tables and Charts
10 Sampling & Empirical Distributions I Project 1 due
11 Sampling & Empirical Distributions II Lab 6: Sampling
12 Hypothesis Testing HW 4 due
Probability and Sampling
13 A/B Testing Lab 7: Assessing Models / Review
14 Causality
15 Midterm Review (Catch-up) Lab catch-up HW 5 due
Hypothesis Testing
16 Midterm
17 Estimation I Lab 8:
Resampling and Bootstrapping
18 Estimation II
19 The Normal Distribution Project 2 due
20 Central Limit Theorem HW 6 due
Confidence Intervals & Sample Size
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
24 Classification I
25 Classification II Lab 11: Classification HW 8 due
Regression Inference, Diagnostics, and Classification
26 Final Review Project 3 due


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).

Related Courses:

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

Similar courses at Columbia

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