As a prerequisite for admission to the Certificate in Applied Data Science (CADS) program, students must earn a B or higher in the following courses, or demonstrate equivalent knowledge:

  • STOR 155: Introductory level knowledge of statistical data analysis methods including correlation, regression, basic probability, hypothesis testing and confident intervals for stat.
  • COMP 110 or INLS 560: Basics of computer programming (language agnostic) ) including flow control, functions, basic data structures, and debugging techniques.


The CADS curriculum will consist of 9 credits of data science coursework and a 3-credit practicum as outlined below:

  • Data Analysis I (1.5)
  • Data Analysis II (3.0)
  • Applied Data Ethics (1.5)
  • Databases for Data Science (1.5)
  • Applied Data Curation and Management for Data Analysts (1.5)
  • Data Science Practicum (3.0)

Course Descriptions

Data Analysis I
This course provides fundamental skills needed to design and implement analysis of structured data sets. Students will learn data analysis techniques using numeric, textual, and tabular data. This course will utilize the Python programming language.

Data Analysis II
An applied course introducing computational statistical analysis, machine learning, data exploration and communication with a focus on applied concepts as encountered within common data science applications. This course will utilize the Python programming language and Jupyter Notebooks.

Applied Data Ethics
This course will introduce students to several ethical issues faced by data scientists in creation, collection, curation, analysis, and use of data. It addresses issues at multiple scales.

Databases for Data Science
Overview of the design and implementation of database systems, focusing on applied topics most relevant to the practice of data science.

Applied Data Curation and Management for Data Analysts
This class will provide an introduction to applied data curation and management. Topics includes the data curation lifecycle; data management; data sharing and reuse; and FAIR data concepts.

Data Science Practicum
Supervised observation and practice applying data science fundamentals to a real-world problem.