Course Description

This course and its co-requisite lab 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. The course 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 college-level math background is required.

Learning Outcomes

• Learn how to explore datasets by visualizing data to identify patterns
• Conduct statistical analyses to draw inferences about the world
• Use data to make predictions about possible outcomes using models
• Gain familiarity with programming in Python and experience analyzing data, building visualizations, and running statistical tests using Jupyter Notebooks

Materials

This course is based on UC Berkeley’s Foundation of Data Science course and their Computational and Inferential Thinking textbook. Students will complete lab and homework assignments using cloud-based JupyterHub notebooks that can be accessed in the browser, so no software will need to be installed as part of this course.

Grading

This course involves weekly labs, homework assignments, a written midterm exam, and a final project. There is no final exam for this course.

Final grades are based on the instructor's holistic evaluation of your performance and follow Barnard's grading system. As a general guideline, assignments are weighted as follows:

• 35% - Homework Assignments
• 25% - Midterm Exam
• 40% - Final Project

Lab Sections

All labs are graded out of 10 points. You receive 5 points for attendance, and 5 points for a fully complete + correct notebook. If your notebook is partially complete / correct but shows effort, you will receive 3 points. To submit your lab, you should ensure that all grader cells have been run (grader.check) and submit a PDF of your notebook to Courseworks.

If you are going to be late for lab or unable to attend, you must email your lab TA in advance. If you don’t come to lab or don’t email the TA in advance, you will receive 0 points for attendance. You are permitted one unexcused absence from lab during the semester.