Data Science Course Schedule spring 2019

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

This fast-paced course gives students fundamental Python knowledge necessary for advanced work in data science. Students gain frequent practice writing code, building to advanced skills focused on data science applications. We introduce a range of Python objects and control structures, then build on these with classes on object-oriented programming. A major programming project reinforces these concepts, giving students insight into how a large piece of software is built and experience managing a full-cycle development project. The last section covers two popular Python packages for data analysis, Numpy and Pandas, and includes an exploratory data analysis.

Section 1
M 4:00 - 5:30 PM
Instructor(s): Gerald Benoît
Section 2
M 6:30 - 8:00 PM
Instructor(s): Gerald Benoît
Section 3
Tu 4:00 - 5:30 PM
Instructor(s): Christopher Llop
Section 4
W 4:00 - 5:30 PM
Instructor(s): Richard Huntsinger
Section 5
Th 6:30 - 8:00 PM
Instructor(s): Gunnar Kleemann

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 4:00 - 5:30 PM
Instructor(s): Corey Jackson
Section 2
Tu 6:30 - 8:00 PM
Instructor(s): Corey Jackson
Section 3
W 4:00 - 5:30 PM
Instructor(s): Michael Rivera
Section 4
W 6:30 - 8:00 PM
Instructor(s): Michael Rivera
Section 5
W 6:30 - 8:00 PM
Instructor(s): Nima Bari
Section 6
Th 6:30 - 8:00 PM
Instructor(s): Nima Bari
Section 7
Th 4:00 - 5:30 PM
Instructor(s): Afshin Nikzad
Section 8
Th 6:30 - 8:00 PM
Instructor(s): Afshin Nikzad

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 6:30 - 8:00 PM
Instructor(s): Eric Penner
Section 2
Tu 6:30 - 8:00 PM
Instructor(s): Eric Penner
Section 3
W 6:30 - 8:00 PM
Instructor(s): Eric Penner
Section 4
W 4:00 - 5:30 pm
Instructor(s): Casper Nordal Joergensen
Section 5
W 6:30 - 8:00 pm
Instructor(s): Casper Nordal Joergensen
Section 6
Section 7
Section 8
TBD
Instructor(s): TBD
Section 9

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.

Section 1
Tu 4:00 - 5:30 PM
Instructor(s): Kevin Crook
Section 2
Tu 6:30 - 8:00 PM
Instructor(s): Kevin Crook
Section 3
W 4:00 - 5:30 PM
Instructor(s): Kevin Crook
Section 4
Th 2:00 - 3:30 PM
Instructor(s): Taylor Martin, Mark Mims
Section 5
Section 6
Section 7
Section 8
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 - 5:30 PM
Instructor(s): Alexander Iliev
Section 2
M 6:30 - 8:00 PM
Instructor(s): Yacov Salomon
Section 3
W 4:00 - 5:30 PM
Instructor(s): June Andrews
Section 4
Tu 4:00 - 5:30 PM
Instructor(s): Amit Bhattacharyya
Section 5
Th 6:30 - 8:00 PM
Instructor(s): Todd Holloway
Section 6
Sat 10:00 - 11:30 AM
Instructor(s): Isabell Konrad
Section 7

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.

Section 1
W 6:30 - 8:00 PM
Instructor(s): Fereshteh Amini
Section 2
Section 3
Section 4

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.
Section 1
M 4:00 - 5:30 PM
Instructor(s): Stanislav Kelman, Joyce Shen
Section 2
M 6:30 - 8:00 PM
Section 3
Tu 4:00 - 5:30 PM
Section 4
Tu 6:30 - 8:00 PM
Section 5
W 4:00 - 5:30 PM
Instructor(s): Joyce Shen, David Steier
Section 6
W 6:30 - 8:00 PM
Instructor(s): Joyce Shen, David Steier

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
Tu 4:00 - 5:30 PM
Instructor(s): Morgan Ames
Section 2
W 4:00 - 5:30 PM
Instructor(s): Morgan Ames
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
Tu 4:00 - 5:30 PM
Instructor(s): Micah Gell-Redman
Section 2
Tu 6:30 - 8:00 PM
Instructor(s): D. Alex Hughes
Section 3
W 4:00 - 5:30 PM
Instructor(s): D. Alex Hughes
Section 4
W 6:30 - 8:00 PM
Instructor(s): Daniel Hedblom
Section 5
Th 4:00 - 5:30 PM
Instructor(s): Micah Gell-Redman
Section 6
Th 6:30 - 8:00 PM
Instructor(s): Daniel Hedblom

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.

Section 1
M 4:00 - 5:30 PM
Section 2
Tu 2:00 - 3:30 PM
Instructor(s): Ryan DeJana, Brad DesAulniers

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
Th 4:00 - 5:30 PM
Instructor(s): Kyle Hamilton
Section 2
Th 6:30 - 8:00 PM
Instructor(s): Kyle Hamilton
Section 3
F 2:00 - 3:30 PM
Instructor(s): Kyle Hamilton
Section 4
F 4:00 - 5:30 PM
Instructor(s): Kyle Hamilton
Section 5
Sat 8:00 - 9:30 AM
Section 6
Sat 10:00 - 11:30 AM

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
Tu 4:00 - 5:30 PM
Instructor(s): Daniel Cer
Section 2
W 6:30 - 8:00 PM
Instructor(s): Blake Lemoine
Section 3
Th 4:00 - 5:30 PM
Instructor(s): Joachim Rahmfeld
Section 4
Th 6:30 - 8:00 PM
Instructor(s): Mark Butler
Section 5
F 4:00 - 5:30 PM
Instructor(s): Sid J Reddy
Section 6

A continuation of Data Science W203 (Exploring and Analyzing Data), 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
M 4:00 - 5:30 PM
Instructor(s): Jeff Yau
Section 2
M 6:30 - 8:00 PM
Instructor(s): Jeff Yau
Section 3
M 8:30 - 10:00 PM
Instructor(s): Jeff Yau