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:
- INLS 761: Data Analysis (1.5 credits)
- INLS 772: Applied Statistics, Machine Learning and Data Communication (3 credits)
- INLS 773: Database for Data Science (1.5 credits)
- INLS 774: Applied Data Ethics (1.5 credits)
- INLS 775: Applied Data Curation and Management (1.5 credits)
- INLS 792: Applied Data Science Practicum (3 credits)
This course provides fundamental skills for developing software for the analysis of structured data sets. Students will learn data analysis techniques using numeric, textual, and tabular data in the context of data science topics such as information retrieval, textual analysis, and basic machine learning.
Applied Statistics, Machine Learning and Data Communication
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.
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 Ethics
Introduction to ethical issues faced by data scientists in creation, collection, curation, and use of data at multiple scales.
Applied Data Curation and Management
Introduction to digital and data curation in a wide array of environments including business, government, and academia.
Applied Data Science Practicum
Builds upon the formal classroom instruction in data science concepts and technologies through a "hands-on" project experience within an industry, non-profit, or other work environment that relates the the student's primary field of study/practice.