Data Science Course Schedule fall 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
Tu 4-5:30 P.M.
Instructor(s): Benjamin Stokes
Section 2
Tu 6:30-8:00 P.M.
Instructor(s): Benjamin Stokes
Section 3
Th 4-5:30 P.M.
Instructor(s): Peter Norlander
Section 4
Th 6:30-8:00 P.M.
Instructor(s): Peter Norlander
Section 5
M 6:30-8:00 P.M.
Instructor(s): Benjamin Stokes, Peter Norlander

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
M 4-5:30 P.M.
Instructor(s): Blaine Robbins, Paul Laskowski
Section 2
M 6:30-8:00 P.M.
Instructor(s): Blaine Robbins
Section 3
Th 4-5:30 P.M.
Instructor(s): Paul Laskowski
Section 4
Th 6:30-8:00 P.M.
Instructor(s): Paul Laskowski
Section 5
Tu 6:30-8:00 P.M.
Instructor(s): Blaine Robbins

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
Th 4-5:30 P.M.
Instructor(s): Alex Miłowski
Section 2
Th 6:30-8:00 P.M.
Instructor(s): Alex Miłowski

Machine learning is a rapidly growing field at the intersection of computer science and statistics concerned with finding patterns in data. It is responsible for tremendous advances in technology, from personalized product recommendations to speech recognition in cell phones. This course provides a broad introduction to the key ideas in machine learning. The emphasis will be on intuition and practical examples rather than theoretical results, though some experience with probability, statistics, and linear algebra will be important.

Section 1
Tu 4-5:30 P.M.
Instructor(s): Daniel Gillick, Dav Clark
Section 2
Tu 6:30-8:00 P.M.
Instructor(s): Daniel Gillick, Dav Clark

Intro to the legal, policy, and ethical implications of data, including privacy, surveillance, security, classification, discrimination, decisional-autonomy, and duties to warn or act. Examines legal, policy, and ethical issues throughout the full data-science life cycle collection, storage, processing, analysis, and use with case studies from criminal justice, national security, health, marketing, politics, education, employment, athletics, and development. Includes legal and policy constraints and considerations for specific domains and data-types, collection methods, and institutions; technical, legal, and market approaches to mitigating and managing concerns; and the strengths and benefits of competing and complementary approaches.

Section 1
M 4-5:30 P.M.
Instructor(s): Deirdre Mulligan
Section 2
M 6:30-8:00 P.M.
Instructor(s): Deirdre Mulligan

This course introduces students to experimentation in the social sciences. This topic has increased considerably in importance since 1995, as researchers have learned to think creatively about how to generate data in more scientific ways, and developments in information technology have facilitated the development of better data gathering. Key to this area of inquiry is the insight that correlation does not necessarily imply causality. In this course, we learn how to use experiments to establish causal effects and how to be appropriately skeptical of findings from observational data.

Section 1
M 4-5:30 P.M.
Instructor(s): David Broockman, David Reiley
Section 2
M 6:30-8:00 P.M.
Instructor(s): David Broockman, David Reiley