UCSB INT 15: Data Science Principles & Techniques
In this course, we will explore the data science lifecycle: question formulation, data collection & cleaning, exploratory data analysis & visualization, statistical inference and prediction, and decision-making.
This site is currently under construction and will be updated with more information soon.
Instructors: Professors Kate Kharitonova (CS) and Alex Franks (PSTAT)
Prerequisites: PSTAT 120B, and one of CS 8, CS16, INT5 or Engineering 3.
Catalog description: Overview and use of data science tools in Python for data retrieval, analysis, visualization, reproducible research and automated report generation. Case studies will illustrate practical use of these tools. This new course will focus on concepts that are relevant for data science by using some of the popular software tools in this area. Doing data science is more than using isolated methods. Creatively using a collection of concepts and domain knowledge is emphasized to clean, transform, analyze, and present data. Concepts in data ethics and privacy will also be discussed. Case studies will illustrate real usage scenarios.
Programming experience: This course is designed for students with a solid conceptual understanding of programming primitives (e.g., flow control, functions, arrays, data types) and is comfortable in at least one programming or scripting language (C/C++, R, Python, etc.).
Software tools: Many software tools are used for data science. Tools we will use for this course include (but not limited to)
- Source code version control (git/github)
- Command line tools
- Statistical/machine learning libraries
- Notebook software for reproducibility
- Interactive visualization in the web browser.
Learning by doing will require software documentation, experimenting by trial-and-error, and debugging.
Enrollment for this course will be limited, and add-codes will be given only after reviewing students’ applications.
Apply to enroll in the course: https://goo.gl/forms/HqTUdNrkvZsoEMOY2
Application process: Please complete the following form by
March 1, 2019 March 9, 2019.
Because admission to the course is contingent on various factors, including the number of applications, each undergraduate applicant should enroll in a “back-up” course that can be dropped if admitted to INT 15 (which you would still be happy to take if you haven’t been admitted).
How is this course different that PSTAT134? This is experimental course that will have significant overlap with the content in PSTAT 134. An emphasis will be placed on learning from each other: unlike 134, this course will be co-taught by professors from CS and PSTAT. Important: Unlike PSTAT134, INT 15 does not satisfy requirements for any majors on campus. We are looking for self-motivated students with diverse interests in the data science.