Data Science Course Schedule fall 2022

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 6:30 pm - 8:00 pm
Instructor(s): Mumin Khan
Section 2
Tu 6:30 pm - 8:00 pm
Instructor(s): Mumin Khan
Section 3
We 6:30 pm - 8:00 pm
Instructor(s): Gunnar Kleemann
Section 5
We 4:00 pm - 5:30 pm
Instructor(s): Uthra Ramanujam
Instructor is Uthra Ramanujam
Instructor is Uthra Ramanujam
Section 6
Th 4:00 pm - 5:30 pm
Instructor(s): Kathy Tian
Instructor is Kat Tian
Instructor is Kat Tian
Section 7
Mo 4:00 pm - 5:30 pm
Instructor(s): Sridevi Pudipeddi
Section 8
Th 6:30 pm - 8:00 pm
Instructor(s): Sridevi Pudipeddi
Section 9
Sa 8:00 am - 9:30 am
Instructor(s): Gerald Benoît
Instructor is Gerry Benoit.
Instructor is Gerry Benoit.
Section 99
Mo 2:00 pm - 3:30 pm
Instructor(s): Gerald Benoît
This section is for 5th Year MIDS ONLY. Instructor is Gerry... more
This section is for 5th Year MIDS ONLY. Instructor is Gerry Benoit.

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): Elena Petrov
Section 2
Mo 6:30 pm - 8:00 pm
Instructor(s): Carlos Rivera
Section 3
Tu 2:00 pm - 3:30 pm
Instructor(s): Anna Zaitsev
Section 4
Tu 4:00 pm - 5:30 pm
Instructor(s): Anna Zaitsev
Section 5
Tu 4:00 pm - 5:30 pm
Instructor(s): Michael Rivera
Section 6
Tu 6:30 pm - 8:00 pm
Instructor(s): Michael Rivera
Section 7
Tu 6:30 pm - 8:00 pm
Instructor(s): Carlos Rivera
Section 8
We 2:00 pm - 3:30 pm
Instructor(s): Brooks Ambrose
Section 9
We 4:00 pm - 5:30 pm
Instructor(s): Brooks Ambrose
Section 10
We 4:00 pm - 5:30 pm
Instructor(s): Michael Rivera
Section 11
Th 6:30 pm - 8:00 pm
Instructor(s): Conor Healy
Section 12
Th 4:00 pm - 5:30 pm
Instructor(s): JP Dolphin

Introduces the data sciences landscape, with a focus on learning data science techniques to uncover and answer questions students will encounter in industry. Lectures, readings, discussions, and assignments will teach how to apply methods to ask better questions, gather data, interpret results, and convey findings to various audiences. The emphasis is on making practical contributions to real decisions that organizations make.

This 4-credit version of the course is designed for early-career learners in our 5th Year MIDS pathway. It provides additional attention to introducing professional and business knowledge and skills, and providing students with opportunities to apply and reflect on learning how to become a data science professional.

Section 1
Tu 5:30 pm - 8:00 pm
Instructor(s): John (J.B.) Blevins
This class is for 5th Year MIDS Students ONLY.
This class is for 5th Year MIDS Students ONLY.
Section 2
We 4:00 pm - 6:30 pm
Instructor(s): Sahab Aslam
This class is for 5th Year MIDS Students ONLY.
This class is for 5th Year MIDS Students ONLY.

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 pm - 5:30 pm
Instructor(s): D. Alex Hughes
Section 2
Tu 4:00 pm - 5:30 pm
Instructor(s): Sushovan Majhi
Section 3
Tu 4:00 pm - 5:30 pm
Instructor(s): Mark Labovitz
Section 4
Tu 6:30 pm - 8:00 pm
Instructor(s): Sushovan Majhi
Section 5
Tu 6:30 pm - 8:00 pm
Instructor(s): Mark Labovitz
Section 6
Th 4:00 pm - 5:30 pm
Instructor(s): Gunnar Kleemann
Section 7
Th 6:30 pm - 8:00 pm
Instructor(s): Gunnar Kleemann
Section 8
We 4:00 pm - 5:30 pm
Instructor(s): Tanya Roosta
Section 9
Th 6:30 pm - 8:00 pm
Instructor(s): Bill Chung
Section 98
Th 2:00 pm - 3:30 pm
Instructor(s): Paul Laskowski
This section is for 5th Year MIDS ONLY.
This section is for 5th Year MIDS ONLY.
Section 99
Th 4:00 pm - 5:30 pm
Instructor(s): Paul Laskowski
This section is for 5th Year MIDS ONLY.
This section is for 5th Year MIDS ONLY.

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): Doris Schioberg
Section 2
Tu 4:00 pm - 5:30 pm
Instructor(s): Korin Reid
Section 3
Tu 6:30 pm - 8:00 pm
Instructor(s): Korin Reid
Section 4
We 6:30 pm - 8:00 pm
Instructor(s): Shiraz Chakraverty
Section 5
Th 2:00 pm - 3:30 pm
Instructor(s): Doris Schioberg
Section 6
Th 4:00 pm - 5:30 pm
Instructor(s): Kevin Crook
Section 7
Th 6:30 pm - 8:00 pm
Instructor(s): Kevin Crook
Section 8
Th 6:30 pm - 8:00 pm
Instructor(s): Doris Schioberg
Section 9
Fr 4:00 pm - 5:30 pm
Instructor(s): Kevin Crook
Section 99
Mo 2:00 pm - 3:30 pm
Instructor(s): Doris Schioberg
This section is for 5th Year MIDS ONLY.
This section is for 5th Year MIDS 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 4:00 pm - 5:30 pm
Instructor(s): Cornelia Ilin
Section 2
Tu 6:30 pm - 8:00 pm
Instructor(s): John Santerre
Section 3
Tu 6:30 pm - 8:00 pm
Instructor(s): Nedelina Teneva
Section 4
We 2:00 pm - 3:30 pm
Instructor(s): Amit Bhattacharyya
Section 5
We 4:00 pm - 5:30 pm
Instructor(s): Cornelia Ilin
Section 6
We 4:00 pm - 5:30 pm
Instructor(s): Ishaani Priyadarshini
Section 7
We 6:30 pm - 8:00 pm
Instructor(s): Ishaani Priyadarshini
Section 8
Th 2:00 pm - 3:30 pm
Instructor(s): Cornelia Ilin
Section 9
Tu 4:00 pm - 5:30 pm
Instructor(s): Rasika Bhalerao
Section 10
Section 11
Sa 10:00 am - 11:30 am
Instructor(s): Uri Schonfeld

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 4:00 pm - 5:30 pm
Instructor(s): Clinton Brownley
Section 2
Mo 6:30 pm - 8:00 pm
Instructor(s): Andy Reagan
Section 3
Mo 6:30 pm - 8:00 pm
Instructor(s): Mak Ahmad
Section 4
We 4:00 pm - 5:30 pm
Instructor(s): Clinton Brownley
Section 5
We 6:30 pm - 8:00 pm
Instructor(s): Mak Ahmad
Section 6
We 6:30 pm - 8:00 pm
Instructor(s): Fereshteh Amini
Instructor is Fereshteh Amini.
Instructor is Fereshteh Amini.
Section 7
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
Mo 2:00 pm - 3:30 pm
Instructor(s): Joyce Shen, Zona Kostic
Section 2
Tu 4:00 pm - 5:30 pm
Instructor(s): Cornelia Ilin, Fred Nugen
Section 3
Tu 4:00 pm - 5:30 pm
Section 4
Tu 6:30 pm - 8:00 pm
Section 5
Tu 6:30 pm - 8:00 pm
Instructor(s): Fred Nugen, Ramesh Sarukkai
Section 6
We 4:00 pm - 5:30 pm
Instructor(s): Joyce Shen, David Steier
Section 7
We 6:30 pm - 8:00 pm
Instructor(s): Joyce Shen, David Steier
Section 8
Th 4:00 pm - 5:30 pm
Section 9
Th 6:30 pm - 8:00 pm
Instructor(s): Fred Nugen, Alberto Todeschini

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 pm - 5:30 pm
Instructor(s): Jared Maslin, Deb Donig
Section 2
Tu 6:30 pm - 8:00 pm
Instructor(s): Jared Maslin, Deb Donig
Section 3
We 6:30 pm - 8:00 pm
Instructor(s): Jared Maslin, Deb Donig

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
Mo 4:00 pm - 5:30 pm
Instructor(s): Daniel Aranki
Section 2
Th 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 6:30 pm - 8:00 pm
Instructor(s): D. Alex Hughes
Section 2
Tu 4:00 pm - 5:30 pm
Instructor(s): Scott Guenther
Section 3
Tu 6:30 pm - 8:00 pm
Instructor(s): Scott Guenther
Section 4
Tu 6:30 pm - 8:00 pm
Instructor(s): David Wheeler
Section 5
We 6:30 pm - 8:00 pm
Instructor(s): Scott Guenther
Section 6
Th 6:30 pm - 8:00 pm
Instructor(s): Scott Guenther
Section 7

This course provides learners hands-on data management and systems engineering experience using containers, cloud, and Kubernetes ecosystems based on current industry practice. The course will be project-based with an emphasis on how production systems are used at leading technology-focused companies and organizations. During the course, learners will build a body of knowledge around data management, architectural design, developing batch and streaming data pipelines, scheduling, and security around data including access management and auditability. We’ll also cover how these tools are changing the technology landscape.

Section 1
Tu 6:30 pm - 8:00 pm
Instructor(s): Amanda Ford
Section 2
We 6:30 pm - 8:00 pm
Instructor(s): Luis Villarreal
Section 3
Sa 8:00 am - 9:30 am
Instructor(s): Amanda Ford

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
Instructor(s): James Shanahan
Section 2
Tu 4:00 pm - 5:30 pm
Instructor(s): James Shanahan
Section 3
Th 6:30 pm - 8:00 pm
Instructor(s): Ramakrishna Gummadi
Section 4
Th 6:30 pm - 8:00 pm
Instructor(s): Vinicio De Sola
Section 5
Fr 2:00 pm - 3:30 pm
Instructor(s): Vinicio De Sola
Section 6
Fr 4:00 pm - 5:30 pm
Instructor(s): Vinicio De Sola
Section 7
Sa 10:00 am - 11:30 am
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
Tu 2:00 pm - 3:30 pm
Instructor(s): Peter Grabowski
Instructor is Peter Grabowski.
Instructor is Peter Grabowski.
Section 2
We 4:00 pm - 5:30 pm
Instructor(s): Natalie Ahn
Section 3
Tu 4:00 pm - 5:30 pm
Instructor(s): Daniel Cer
Section 4
We 4:00 pm - 5:30 pm
Instructor(s): Mark Butler
Section 5
Mo 6:30 pm - 8:00 pm
Instructor(s): Jennifer Zhu
Section 6
We 6:30 pm - 8:00 pm
Instructor(s): Mike Tamir, Paul Spiegelhalter

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
Section 2
Tu 6:30 pm - 8:00 pm

This course introduces the theoretical and practical aspects of computer vision, covering both classical and state of the art deep-learning based approaches. This course covers everything from the basics of the image formation process in digital cameras and biological systems, through a mathematical and practical treatment of basic image processing, space/frequency representations, classical computer vision techniques for making 3-D measurements from images, and modern deep-learning based techniques for image classification and recognition.

Section 1
We 4:00 pm - 5:30 pm
Instructor(s): Rachel Brown
Section 2
Fr 4:00 pm - 5:30 pm
Instructor(s): Shruti Agarwal
Section 3
Tu 6:30 pm - 8:00 pm
Instructor(s): Allen Yang

This is a multi-topic and multidisciplinary course that explores contemporary and futuristic topics that are intertwined with data and data applications. This course synthesizes data and AI research, enterprise technology strategy, data business model, and market analysis into one. Students are required to participate in weekly sessions and write response pieces as well as a final paper studying one defining data application or case study end-to-end. Students taking this course will further broaden and deepen their holistic understanding of current topics in data applications and will gain the skills to systematically and rigorously analyze any data application now or emerging ones in the future in various industries.  This is the course for you if you want to develop a depth of understanding on both the technical side and the applied side in various contemporary domains and markets, and how the modern applications impact data science practitioners and the general population.

Section 1
TuTh 6:30 pm - 8:00 pm
Instructor(s): Joyce Shen
Pre-requisites: MIDS Students Only. DATASCI W205 and... more
Pre-requisites: MIDS Students Only. DATASCI W205 and DATASCI W207. This course cannot be taken alongside DATASCI 210: Capstone. Please note that this Special Topics Course will differ from our regular MIDS course format in that it will not have pre-recorded asynchronous lecture videos. Instead, it will require two 90 minute class sections per week.