DSCI 101

DSCI 101 is an introductory course intended to provide students with an understanding of fundamental concepts in data science. This is the first of two foundational courses, the next course in the series is DSCI 102.

This course utilizes a quantitative approach to explore fundamental concepts in data science.

Students will develop key skills in programming and statistical inference as they interact with real-world data sets across a variety of domains. Ethical ramifications of data collection, data-driven decision-making, and privacy will be explored. This course is designed to be accessible to students without prior experience in computer programming or statistics.

Lectures and Labs

Two 80-minute lectures are delivered each week. Mandatory attendance at a 110-minute lab each week is also required.


None - This course is designed for entry-level students from any major. It is explicitly designed for students who have not previously taken statistics or computer science courses. No math beyond basic addition.

Textbooks and Readings

Textbook: Computational and Inferential Thinking: The Foundations of Data Science, a free online textbook that includes Jupyter notebooks and public data sets for its examples.

Expected Learning Outcomes

Upon successful completion of this course each student should be able to:

  • Discover, organize, analyze, and visualize data using the Python programming language
  • Identify potential errors in data collection and analysis and discuss their consequences
  • Apply concepts of statistical inference including sampling and simulation to create and test models
  • Develop and test null and alternative hypotheses to answer domain-specific questions
  • Outline ethical ramifications of data collection, data-driven decision making, data sharing, and privacy

Course Requirements and Grading

This course will be taught as two 80-minute live lectures and one 110-minute lab each week. Aside from the required textbook, all course materials (project assignments, lab assignments, tutorial videos, sample exam material) will be available from Canvas. We will be using Slack for asynchronous questions and answers.

Grading will be based on the following criteria:

Percentage Component
6 Lab attendance (8 x 0.75%)
16 Lab assignments (8 x 2%)
28 Homework (7 x 4%)
10 Project 1 (1 x 10%)
10 Project 2 (1 x 10%)
10 Quizzes (2 x 5%)
20 Final (1 x 20%)

DSCI majors must take DSCI 101 graded; all others may take it graded or P/N

Grading Scale

  +   -
A 96.67-100.0 93.34-96.66 90.00-93.33
B 86.67-89.99 83.34-86.66 80.00-83.33
C 76.67-79.99 73.34-76.66 70.00-73.33
D 66.67-69.99 63.34-66.66 60.00-63.33
F   0.00-59.99