Data Science Course Schedule summer 2014

Data Science courses are restricted to students enrolled in the MIDS degree program only.

All times are listed in the Pacific Time Zone (America/Los_Angeles).

Graduate

Introduces the data sciences landscape, with a particular focus on learning data science techniques to uncover and answer the questions students will encounter in industry. Lectures, readings, discussions, and assignments will teach how to apply disciplined, creative methods to ask better questions, gather data, interpret results, and convey findings to various audiences. The emphasis throughout is on making practical contributions to real decisions that organizations will and should make.

Section 1
Th 4:30-6 P.M.
Instructor(s): Jonathan Star
Section 2
Th 6:30-8 P.M.
Instructor(s): Jonathan Star

An introduction to many different types of quantitative research methods and statistical techniques for analyzing data. We begin with a focus on measurement, inferential statistics and causal inference using the open-source statistics language, R. Topics in quantitative techniques include: descriptive and inferential statistics, sampling, experimental design, tests of difference, ordinary least squares regression, general linear models.

Section 1
Tu 4:30-6 P.M.
Instructor(s): Paul Laskowski
Section 2
Tu 6:30-8:00 P.M.
Instructor(s): Paul Laskowski

Storing, managing, and processing datasets are foundational processes in data science. This course introduces the fundamental knowledge and skills of data engineering that are required to be effective as a data scientist. This course focuses on the basics of data pipelines, data pipeline flows and associated business use cases, and how organizations derive value from data and data engineering. As these fundamentals of data engineering are introduced, learners will interact with data and data processes at various stages in the pipeline, understand key data engineering tools and platforms, and use and connect critical technologies through which one can construct storage and processing architectures that underpin data science applications.

Section 1
W 4-5:30 P.M.
Instructor(s): Jerry Ye, Ray Larson
Section 2
W 6-7:30 P.M.
Instructor(s): Jerry Ye, Ray Larson

Visualization enhances exploratory analysis as well as efficient communication of data results. This course focuses on the design of visual representations of data in order to discover patterns, answer questions, convey findings, drive decisions, and provide persuasive evidence. The goal is to give you the practical knowledge you need to create effective tools for both exploring and explaining your data. Exercises throughout the course provide a hands-on experience using relevant programming libraries and software tools to apply research and design concepts learned.

Section 1
Tu 6:30-8:00 P.M.
Instructor(s): Annette Greiner
Section 2
Th 5-6:30 P.M.
Instructor(s): Annette Greiner