The University of Chicago Summer
Computer Science for Data Science

Computer Science for Data Science


Course Code

DATA 12000 20

Course Description

This course teaches computational thinking and programming skills to students in the Data Science program.

Topics include control structures and basic data types, abstraction and functional decomposition, classes and objects in Python, basic algorithms, and an introduction to computer structure and low level representation of data types.

Examples will include the application of tools such as scraping web pages and rudimentary machine learning to a variety of fields.

Course Criteria

Prerequisite(s): DATA 11800

Instructor(s)

Kriti Sehgal

Session

September Term

Course Dates

August 24th - September 11th

Class Days

Mon, Tue, Wed, Thu, Fri

Class Time

9:00 am - 11:00 am

Modality

Remote

Other Courses to Consider

These courses might also be of interest.

  • Introduction to Data Science I
    Introduction to Data Science I

    Data science provides tools for gaining insight into specific problems using data, through computation, statistics and visualization. This course introduces students to all aspects of a data analysis process: from posing questions, designing data collection strategies, management+storing and processing of data, exploratory tools to visualization, statistical inference, prediction, interpretation and communication of results. Simple techniques for data analysis are used to illustrate both effective and fallacious uses of data science tools. Although this course is designed to be at the level of mathematical sciences courses in the Core, with little background required, you will develop computational skills that will allow you to analyze data. Computation will be done using Python and Jupyter Notebook.

    Remote
  • Introduction to Data Science II
    Introduction to Data Science II

    This course is the second of a two-quarter systematic introduction to the foundations of data science, as well as to practical considerations in data analysis.

    A broad background on probability and statistical methodology will be provided. More advanced topics on data privacy and ethics, reproducibility in science, data encryption, and basic machine learning will be introduced. We will explore these concepts with real-world problems from different domains.

    Remote