The Minor in Data Science program aims to produce data literate graduates by equipping the students across the different disciplines with a working knowledge of statistics, probability, and computation enabling them to design and execute precise computational and inferential data analysis for their discipline.
By the end of the program, learners will be able to:
|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|
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.
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.
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.
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.
May I take more than one course per term?