Data Science Course Schedule summer 2015

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).

Foundation

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.

Course must be taken for a letter grade to fulfill degree requirements.

Section: 1
M 4-5:30 P.M.
Instructor(s): Peter Norlander
Section: 2
M 6:30-8:00 P.M.
Instructor(s): Peter Norlander
Section: 3
Tu 4-5:30 P.M.
Instructor(s): Brooks Ambrose
Section: 4
Tu 6:30-8:00 P.M.
Instructor(s): Brooks Ambrose

The goal of this course is to provide students with 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.

(Prior to Fall 2016, the course was titled “Exploring and Analyzing Data.”)

Course must be taken for a letter grade to fulfill degree requirements.

Section: 1
W 4-5:30 P.M.
Instructor(s): Blaine Robbins
Section: 2
W 6:30-8:00 P.M.
Instructor(s): Blaine Robbins
Section: 3
Th 4-5:30 P.M.
Instructor(s): Ali Sanaei
Section: 4
Th 6:30-8:00 P.M.
Instructor(s): Ali Sanaei

Data Science depends on data, and a core competency mandated by this reliance on data is knowing effective and efficient ways to manage, search and compute over that data. This course is focused on how data can be stored, managed and retrieved as needed for use in analysis or operations. The goal of this course is provide students with both theoretical knowledge and practical experience leading to mastery of data management, storage and retrieval with very large-scale data sets.

Course must be taken for a letter grade to fulfill degree requirements.

Section: 1
M 4-5:30 P.M.
Instructor(s): Arash Nourian
Section: 2
M 6:30-8:00 P.M.
Instructor(s): Arash Nourian
Section: 3
Tu 4-5:30 P.M.
Instructor(s): Arash Nourian

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.

Course must be taken for a letter grade to fulfill degree requirements.

Section: 1
M 4-5:30 P.M.
Instructor(s): Todd Holloway
Section: 2
M 6:30-8:00 P.M.
Instructor(s): Todd Holloway
Section: 3
Tu 4-5:30 P.M.
Instructor(s): Daniel Percival
Section: 4
Tu 6:30-8:00 P.M.
Instructor(s): Alex Gilgur
Section: 5
Th 4-5:30 P.M.
Instructor(s): Daniel Percival
Section: 6
Th 6:30-8:00 P.M.
Instructor(s): Alex Gilgur

Communicating clearly and effectively about the patterns we find in data is a key skill for a successful data scientist. This course focuses on the design and implementation of complementary visual and verbal representations of patterns and analyses in order to convey findings, answer questions, drive decisions, and provide persuasive evidence supported by data. Assignments will give hands-on experience designing data graphics and visualizations, and reporting findings in prose.

Course must be taken for a letter grade to fulfill degree requirements.

Section: 1
Tu 4-5:30 P.M.
Instructor(s): Annette Greiner
Section: 2
Tu 6:30-8:00 P.M.
Instructor(s): Annette Greiner

Capstone

The capstone course will cement skills learned throughout the MIDS program — both core data science skills and “soft skills” like problem-solving, communication, influencing, and management — preparing students for success in the field. The centerpiece is a semester-long group project in which teams of students propose and select project ideas, conduct and communicate their work, receive and provide feedback (in informal group discussions and formal class presentations), and deliver compelling presentations along with a web-based final deliverable. Includes relevant readings, case discussions, and real-world examples and perspectives from panel discussions with leading data science experts and industry practitioners..

Course must be taken for a letter grade to fulfill degree requirements.

Section: 1
M 4-5:30 P.M.
Instructor(s): Alex Marrs, Ben Gimpert
Section: 2
M 6:30-8:00 P.M.
Instructor(s): Alex Marrs, Ben Gimpert

Advanced

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
W 4-5:30
Instructor(s): Anna Lauren Hoffmann
Section: 2
W 6:30-8:00 P.M.
Instructor(s): Anna Lauren Hoffmann
Section: 4
Th 6:30-8:00 P.M.
Instructor(s): Nathaniel Good
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
W 4-5:30
Instructor(s): Coco Krumme
Section: 2
W 6:30-8:00 P.M.
Instructor(s): Coco Krumme
Section: 3
Th 4-5:30 P.M.
Instructor(s): D. Alex Hughes
Section: 4
Th 6:30-8 P.M.
Instructor(s): D. Alex Hughes

An overview of the contemporary toolkits for problems related to cloud computing and big data. Because the class is an advanced course, we generally assume familiarity with the concepts and spend more time on the implementation. Every lecture is followed by a hands-on assignment, where students get to experience some of the technologies covered in the lecture. By the time students complete the course, they should be able to name the big data problem they are facing, select proper tooling, and know enough to start applying it.

Section: 1
W 4-5:30 P.M.
Section: 2
W 6:30-8 P.M.
Instructor(s): Brad DesAulniers, Michael Dye
Section: 3
Th 6:30-8:00 P.M.
Instructor(s): Jonathan Dye, Thanh Pham