Foundations of Data Science

Learn about data collection, exploration, manipulation and storage, analysis, and presentation to navigate data-rich workplace environments.

About the Certificate

The School of Information directly engages issues at the intersection of technology, people, and information. Through transdisciplinary curriculum, the 9-credit hour Foundations of Data Science graduate certificate aims to train graduate students who wish for a focused amount of foundational data science training as a gateway into a full graduate degree, as an augmenting experience to combine with other graduate degree endeavors, or as a stand-alone training to aid them in their professional lives. This certificate encompasses foundational work in data science.

Learning Outcomes

The Data science graduates often end up working in jobs such as data analyst, data architect, data engineer, solutions architect, and systems engineer.

Students will accomplish the following Learning Outcomes:

Students will demonstrate skills in processing and analyzing data

Students will communicate with and effectively work and interact in teams

Students will demonstrate abilities in analyzing ethical concerns and societal impacts related to data science


Applicants are expected to have completed undergraduate coursework or have professional experience in programming and statistics. Coursework in calculus is preferred but not required. Applicants should be able to speak to their experience in quantitative and analytical reasoning abilities, as well as experience with math and programming including data structures, analysis of algorithms, and linear algebra.

The Foundations of Data Science certificate requires 9 units (3 courses) in core informational areas. These courses can overlap entirely with the MS INFO degree if students are dual enrolled. Students can complete the certificate in 1-2 semesters.

Required Courses

  • 9 units

This course presents an overview and understanding of the intractable and pressing ethical issues as well as their related policies in the information fields. Emerging technological developments in relation to public interests and individual well-being are highlighted throughout the course. Special emphasis is placed on case studies and outcomes as well as frameworks for ethical decision-making.

This course will introduce students to the concepts and techniques of data mining for knowledge discovery. It includes methods developed in the fields of statistics, large-scale data analytics, machine learning, pattern recognition, database technology and artificial intelligence for automatic or semi-automatic analysis of large quantities of data to extract previously unknown interesting patterns. Topics include understanding varieties of data, data preprocessing, classification, association and correlation rule analysis, cluster analysis, outlier detection, and data mining trends and research frontiers. We will use software packages for data mining, explaining the underlying algorithms and their use and limitations. The course include laboratory exercises, with data mining case studies using data from many different resources such as social networks, linguistics, geo-spatial applications, marketing and/or psychology.

Transfer Units

You can transfer up to 3 units from other accredited institutions with the approval of the certificate advisor.

Any course substitutions must be approved in advance by the certificate advisor.

Up to 9 units can be shared between the certificate and Master's degree.