Program Outcomes

By the end of the program, learners will be able to:

• Demonstrate their skills in data collection, processing, and visualization through
• Create and design multiple variations of visualization appropriate for the audience, task, and data using various visualization software and tools
• Design and choose the correct data processing and machine learning techniques needed for specific real-world problems

Course List

Course	Units
Principles of Data Science (DATA100)	3 units
Data Visualization (DATA101)	3 units
Data Mining and Statistics (DATA102)	3 units
Introduction to Machine Learning (DATA103)	3 units
Total	12 units

Course Details

Principles of Data Science (DATA100)

This is an introductory course designed to provide students with the basic concepts of data analysis and statistical computing to explore interesting issues and problems. The course is designed for entry-level students from any major, specifically for students who have not previously taken any statistics or computer science courses.

Data Visualization (DATA101)

Pre-requisite: DATA100

This course explores the design and creation of data visualizations based on available data and tasks to be achieved. This process includes basic data modeling, processing, mapping data attributes to graphical attributes, and strategic visual encoding based on known properties of visual perception as well as the task(s) at hand. Students will also learn to evaluate the effectiveness of visualization designs, and think critically about each design decision, such as choice of color and visual encoding.

Data Mining and Statistics (DATA102)

Pre-requisite: DATA100

This course studies algorithms and computational paradigms that allow computers to find patterns and regularities in databases, and generally improve their performance through interaction with data. This course includes data selection, cleaning, and using different statistical techniques. The course will cover all these issues and will illustrate the whole process with examples. Data mining mostly handles tabular data because of its roots in knowledge discovery in databases, but it is not limited to it.

Introduction to Machine Learning (DATA103)

Pre-requisite: DATA100

Machine learning is the automatic induction of new information from large amounts of data to make predictions or decisions without human intervention. This course introduces the students to a broad cross-section of models and algorithms for machine learning, and equips them with skills to discover new information from volumes of data. Data mining and machine learning have overlapping algorithms and methods, but they focus on different things: data mining focuses on finding patterns while machine learning focuses on predictive models.

Schedule of Course Offerings

Term 1

• Principles of Data Science (DATA100)
• Introduction to Machine Learning (DATA103)

Term 2

• Data Visualization (DATA101)

Term 3

• Data Mining and Statistics (DATA102)

Course Overlap Rules

• A maximum of one course offered by other departments may be credited toward the Minor in Data Science, e.g. DATA103 Introduction to Machine Learning under Data Science Institute overlaps with MCLEARN Machine Learning under Software Technology Department.
• A student should apply for course overlap as soon as the course grade becomes available in the MLS. The deadline for course overlap application is every 6th Monday of the term. Results of the application will be released on the 8th Monday of the term.

FAQs

May I take more than one course per term?

• Following the guidelines for the free minors and electives, you may only enroll in one free course per term. If you decide to take two courses together during a single term, the other course will be paid.