Data Science Course Schedule fall 2016

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
M 4:00-5:30pm Pacific
Instructor(s): Brooks Ambrose
Section 2
Tu 4:00-5:30pm Pacific
Instructor(s): Brooks Ambrose
Section 3
Tu 6:30-8:00pm Pacific
Instructor(s): Brooks Ambrose
Section 4
W 4:00-5:30pm Pacific
Instructor(s): Charles Gomez
Section 5
W 6:30-8:00pm Pacific
Instructor(s): Charles Gomez
Section 6
M 4-5:30 pm Pacific
Instructor(s): Yoonjung Lee

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 4:00-5:30pm Pacific
Instructor(s): Paul Laskowski
Section 2
W 4:00-5:30pm Pacific
Instructor(s): Jennifer Shin
Section 3
W 6:30-8:00pm Pacific
Instructor(s): Jennifer Shin
Section 4
Th 4:00-5:30pm
Instructor(s): Ryan Kappedal, Paul Laskowski
Section 5
Th 6:30-8pm Pacific
Instructor(s): Ryan Kappedal
Section 6
W 4-5:30 pm Pacific
Instructor(s): Ali Sanaei

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 4:00-5:30pm
Instructor(s): Amit Bhattacharyya
Section 2
M 6:30-8:00pm Pacific
Instructor(s): Arash Nourian
Section 3
Tu 6:30-8:00pm Pacific
Instructor(s): Jari Koister, Edward Fine
Section 4
W 4:00-5:30pm Pacific
Instructor(s): Uri Schonfeld
Section 5
W 6:30-8:00pm Pacific
Instructor(s): Uri Schonfeld

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
M 4:00-5:30pm Pacific
Instructor(s): Daniel Percival
Section 2
M 6:30-8:00pm Pacific
Instructor(s): Daniel Percival
Section 3
F 4-5:30 pm Pacific
Instructor(s): Todd Holloway
Section 4
F 6:30-8:00pm Pacific
Instructor(s): Todd Holloway

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 4:00-5:30pm Pacific
Instructor(s): Andy Reagan
Section 2
Tu 6:30-8:00pm Pacific
Instructor(s): Daniel Perry
Section 3
W 4-5:30 pm Pacific
Instructor(s): Andy Reagan

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.

Section 1
W 4:00-5:30pm Pacific
Instructor(s): David Steier, Coco Krumme
Section 2
W 6:30-8:00pm Pacific
Instructor(s): Joyce Shen

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.

Th 6:30-8:00pm
Instructor(s): Nathan 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
Th 4:00-5:30pm
Instructor(s): D. Alex Hughes
Section 2
Th 6:30-8:00pm
Instructor(s): D. Alex Hughes

This hands-on course introduces data scientists to technologies related to building and operating live, high throughput deep learning applications running on powerful servers in the cloud as well on smaller and lower power devices at the edge of the network. The material of the class is a set of practical approaches, code recipes, and lessons learned. It is based on the latest developments in the industry and industry use cases as opposed to pure theory. It is taught by professionals with decades of industry experience.

M 4:00-5:30pm Pacific
Instructor(s): Dima Rekesh

This course teaches the underlying principles required to develop scalable machine learning pipelines for structured and unstructured data at the petabyte scale. Students will gain hands-on experience in Apache Hadoop and Apache Spark.

Section 1
W 4:00-5:30pm Pacific
Instructor(s): Mike Tamir, James Shanahan
Section 2
W 6:30-8:00pm Pacific
Instructor(s): Mike Tamir, James Shanahan

Understanding language is fundamental to human interaction. Our brains have evolved language-specific circuitry that helps us learn it very quickly; however, this also means that we have great difficulty explaining how exactly meaning arises from sounds and symbols. This course is a broad introduction to linguistic phenomena and our attempts to analyze them with machine learning. We will cover a wide range of concepts with a focus on practical applications such as information extraction, machine translation, sentiment analysis, and summarization.

Section 1
W 6:30-8:00pm Pacific
Instructor(s): Ian Tenney
Section 2
F 4:00-5:30pm Pacific
Instructor(s): James Kunz

A continuation of Data Science 203 (Statistics for Data Science), this course trains data science students to apply more advanced methods from regression analysis and time series models. Central topics include linear regression, causal inference, identification strategies, and a wide-range of time series models that are frequently used by industry professionals. Throughout the course, we emphasize choosing, applying, and implementing statistical techniques to capture key patterns and generate insight from data. Students who successfully complete this course will be able to distinguish between appropriate and inappropriate techniques given the problem under consideration, the data available, and the given timeframe.

Section 1
Tu 6:30-8:00pm Pacific
Instructor(s): Devesh Tiwari, Jeffrey Yau
Section 2
F 6:30-8:00pm
Instructor(s): Devesh Tiwari
Section 3
Tu 4-5:30 pm Pacific
Instructor(s): Jeffrey Yau