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STAT 161 Introduction to Data Science Units: 3.00
This course introduces critical concepts, tools, techniques and skills in statistical inference/learning, machine learning, and computer programming, through hands-on analysis of real-world datasets from diverse fields in science and social science. It offers three perspectives (inferential thinking, computational thinking and real-world relevance) on the foundations of Data Science and develops a data-oriented mindset.
Learning Hours: 120 (36 Lecture, 84 Private Study)
Requirements: Prerequisite None.
Offering Faculty: Faculty of Arts and Science
Course Learning Outcomes:
- Work with critical concepts, tools, techniques, and skills in computer programming, statistical inference/learning and machine learning.
- Use visualization to understand data.
- Work with the computational tools and practices for summary, analysis, and visualization of data.
- Analyze real data sets and communicate their results.
- Have a basic understanding of the implications and tools of data collection.