Data Science Course Schedule summer 2021

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

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
Mo 4:00 pm - 5:30 pm
Instructor(s): Gunnar Kleemann
Section 2
Mo 6:30 pm - 8:00 pm
Instructor(s): Gunnar Kleemann
Section 3
Mo 6:30 pm - 8:00 pm
Instructor(s): Tracy Huang
Section 4
Tu 4:00 pm - 5:30 pm
Instructor(s): Neha Singh
Section 5
Tu 6:30 pm - 8:00 pm
Instructor(s): Mahnaz Roshanaei
Section 6
We 4:00 pm - 5:30 pm
Instructor(s): Neha Singh
Section 7
We 6:30 pm - 8:00 pm
Instructor(s): Tracy Huang
Section 8
Th 6:30 pm - 8:00 pm
Instructor(s): Gunnar Kleemann
Section 9
Fr 6:30 pm - 8:00 pm
Instructor(s): Tracy Huang
Section 98
Tu 4:00 pm - 5:30 pm
Instructor(s): Mumin Khan
5th Year MIDS Students ONLY
5th Year MIDS Students ONLY
Section 99
Fr 2:00 pm - 3:30 pm
Instructor(s): Michael Pritchard
5th Year MIDS Students ONLY
5th Year MIDS Students ONLY

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
Mo 4:00 pm - 5:30 pm
Instructor(s): Carlos Rivera
Section 2
Tu 4:00 pm - 5:30 pm
Instructor(s): Carlos Rivera
Section 3
Tu 6:30 pm - 8:00 pm
Instructor(s): Brooks Ambrose
Section 4
Th 6:30 pm - 8:00 pm
Instructor(s): Brooks Ambrose
Section 5
Th 4:00 pm - 5:30 pm
Instructor(s): Felipe Campos
Section 6
Fr 4:00 pm - 5:30 pm
Instructor(s): Conor Healy
Section 7
Fr 6:30 pm - 8:00 pm
Instructor(s): Conor Healy
Section 8
Sa 8:00 am - 9:30 am
Instructor(s): Elena Petrov
Section 9
Sa 10:00 am - 11:30 am
Instructor(s): JP Dolphin
Section 10
We 4:00 pm - 5:30 pm
Instructor(s): Sahab Aslam
Section 11
We 6:30 pm - 8:00 pm
Section 98
We 2:00 pm - 3:30 pm
Instructor(s): Anna Zaitsev
5th Year MIDS Students ONLY. Fifth-year sections of MIDS... more
5th Year MIDS Students ONLY. Fifth-year sections of MIDS 201 will meet once a week; however, there are two weeks (week 4 and week 9) in the term where sections will meet twice. These additional sessions are required. The instructor will announce this and will coordinate at the beginning of the term to find a time that works for all students.
Section 99
We 4:00 pm - 5:30 pm
Instructor(s): Anna Zaitsev
5th Year MIDS Students ONLY. Fifth-year sections of MIDS... more
5th Year MIDS Students ONLY. Fifth-year sections of MIDS 201 will meet once a week; however, there are two weeks (week 4 and week 9) in the term where sections will meet twice. These additional sessions are required. The instructor will announce this and will coordinate at the beginning of the term to find a time that works for all students.

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
Mo 2:00 pm - 3:30 pm
Instructor(s): Paul Laskowski
Section 2
Tu 2:00 pm - 3:30 pm
Instructor(s): Sushovan Majhi
Section 3
Tu 4:00 pm - 5:30 pm
Instructor(s): Sushovan Majhi
Section 4
Tu 4:00 pm - 5:30 pm
Instructor(s): D. Alex Hughes
Section 5
Tu 6:30 pm - 8:00 pm
Instructor(s): D. Alex Hughes
Section 6
We 4:00 pm - 5:30 pm
Instructor(s): Lee Moore
Section 7
We 6:30 pm - 8:00 pm
Instructor(s): Lee Moore
Section 8
Th 4:00 pm - 5:30 pm
Instructor(s): Mark Labovitz
Section 9
Sa 10:00 am - 11:30 am
Instructor(s): Mark Labovitz
Section 10
Th 6:30 pm - 8:00 pm
Instructor(s): Todd Young
Section 11
Sa 8:00 am - 9:30 am
Instructor(s): Mark Labovitz
Section 12
Th 6:30 pm - 8:00 pm
Instructor(s): Paul Laskowski, Bill Chung
This course will be co-taught by Paul Laskowski and Bill... more
This course will be co-taught by Paul Laskowski and Bill Chung.

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
Mo 6:30 pm - 8:00 pm
Instructor(s): Shiraz Chakraverty
Section 2
Tu 6:30 pm - 8:00 pm
Instructor(s): Shiraz Chakraverty
Section 3
We 6:30 pm - 8:00 pm
Instructor(s): Shiraz Chakraverty
Section 4
Th 4:00 pm - 5:30 pm
Instructor(s): Doris Schioberg
Section 5
Th 6:30 pm - 8:00 pm
Instructor(s): Doris Schioberg
Section 6
Mo 4:00 pm - 5:30 pm
Instructor(s): Kevin Crook
Section 7
Tu 4:00 pm - 5:30 pm
Instructor(s): Kevin Crook
Section 8
Th 2:00 pm - 3:30 pm
Instructor(s): Mark Mims, Taylor Martin
Section 9
Fr 2:00 pm - 3:30 pm
Instructor(s): Vinicio De Sola
Section 10
Fr 4:00 pm - 5:30 pm
Instructor(s): Vinicio De Sola
Section 11
We 4:00 pm - 5:30 pm
Instructor(s): Jari Koister
Section 99
Th 2:00 pm - 3:30 pm
Instructor(s): Doris Schioberg
5th Year MIDS Students ONLY
5th Year MIDS Students ONLY

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
Mo 6:30 pm - 8:00 pm
Instructor(s): Yacov Salomon
Section 2
Tu 2:00 pm - 3:30 pm
Instructor(s): Amit Bhattacharyya
Section 3
Tu 6:30 pm - 8:00 pm
Instructor(s): Todd Holloway
Section 4
We 4:00 pm - 5:30 pm
Instructor(s): Doris Schioberg
Section 5
We 6:30 pm - 8:00 pm
Instructor(s): Doris Schioberg
Section 6
Th 4:00 pm - 5:30 pm
Instructor(s): Cornelia Paulik
Section 7
Th 6:30 pm - 8:00 pm
Instructor(s): Cornelia Paulik
Section 8
Fr 6:30 pm - 8:00 pm
Instructor(s): Uri Schonfeld
Section 9
Sa 10:00 am - 11:30 am
Instructor(s): Uri Schonfeld
Section 10
Tu 4:00 pm - 5:30 pm
Instructor(s): Peter Grabowski

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
Mo 6:30 pm - 8:00 pm
Instructor(s): Andy Reagan
Section 2
Tu 6:30 pm - 8:00 pm
Instructor(s): Fereshteh Amini
Section 3
We 4:00 pm - 5:30 pm
Instructor(s): Clinton Brownley
Section 4
Th 4:00 pm - 5:30 pm
Instructor(s): Bum Chul Kwon

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
Tu 4:00 pm - 5:30 pm
Section 2
Tu 6:30 pm - 8:00 pm
Section 3
We 4:00 pm - 5:30 pm
Instructor(s): David Steier, Joyce Shen
Section 4
We 6:30 pm - 8:00 pm
Instructor(s): Joyce Shen, David Steier
Section 5
Th 6:30 pm - 8:00 pm
Instructor(s): Alberto Todeschini, Fred Nugen
Section 6
We 6:30 pm - 8:00 pm
Section 97
Tu 2:00 pm - 3:30 pm
Instructor(s): Joyce Shen, Fred Nugen
5th Year MIDS students ONLY
5th Year MIDS students ONLY
Section 98
Th 2:00 pm - 3:30 pm
Instructor(s): Fred Nugen, Zona Kostic
5th Year MIDS students ONLY
5th Year MIDS students ONLY
Section 99
Th 4:00 pm - 5:30 pm
Instructor(s): Fred Nugen, Alberto Todeschini
5th Year MIDS students ONLY
5th Year MIDS students ONLY

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 6:30 pm - 8:00 pm
Section 2
We 4:00 pm - 5:30 pm
Section 97
Tu 4:00 pm - 5:30 pm
5th Year MIDS students ONLY
5th Year MIDS students ONLY
Section 98
We 2:00 pm - 3:30 pm
5th Year MIDS students ONLY
5th Year MIDS students ONLY
Section 99
We 4:00 pm - 5:30 pm
5th Year MIDS students ONLY
5th Year MIDS students ONLY

This course surveys privacy mechanisms applicable to systems engineering, with a particular focus on the inference threat arising due to advancements in artificial intelligence and machine learning. We will briefly discuss the history of privacy and compare two major examples of general legal frameworks for privacy from the United States and the European Union. We then survey three design frameworks of privacy that may be used to guide the design of privacy-aware information systems. Finally, we survey threat-specific technical privacy frameworks and discuss their applicability in different settings, including statistical privacy with randomized responses, anonymization techniques, semantic privacy models, and technical privacy mechanisms.

Section 1
Tu 4:00 pm - 5:30 pm
Instructor(s): Daniel Aranki

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 pm - 5:30 pm
Instructor(s): Micah Gell-Redman
Section 2
We 4:00 pm - 5:30 pm
Instructor(s): Scott Guenther
Section 3
We 6:30 pm - 8:00 pm
Instructor(s): Scott Guenther
Section 4
Th 4:00 pm - 5:30 pm
Instructor(s): Micah Gell-Redman
Section 5
Th 2:00 pm - 3:30 pm
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.

Section 1
Mo 6:30 pm - 8:00 pm
Section 2
Tu 2:00 pm - 3:30 pm
Section 3
We 4:00 pm - 5:30 pm
Instructor(s): Ryan DeJana

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
Tu 2:00 pm - 3:30 pm
Section 2
Tu 4:00 pm - 5:30 pm
Section 3
We 2:00 pm - 3:30 pm
Instructor(s): Vinicio De Sola
Section 4
We 6:30 pm - 8:00 pm
Instructor(s): Vinicio De Sola
Section 5
Th 6:30 pm - 8:00 pm
Instructor(s): Ramakrishna Gummadi

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
Mo 6:30 pm - 8:00 pm
Instructor(s): Joachim Rahmfeld
Section 2
Tu 2:00 pm - 3:30 pm
Instructor(s): Peter Grabowski
Section 3
Tu 4:00 pm - 5:30 pm
Instructor(s): Daniel Cer
Section 4
We 6:30 pm - 8:00 pm
Instructor(s): Mike Tamir, Paul Spiegelhalter
Section 5
Th 4:00 pm - 5:30 pm
Instructor(s): Zachary Alexander
Section 6
Fr 4:00 pm - 5:30 pm
Instructor(s): Mark Butler

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 4:00 pm - 5:30 pm
Instructor(s): Jeffrey Yau
Section 2
We 6:30 pm - 8:00 pm
Instructor(s): Gerard Kelly