Data Science Course Schedule fall 2023

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): Uthra Ramanujam
Section 2
Tu 2:00 pm - 3:30 pm
Instructor(s): Gerald Benoît
Section 3
Tu 4:00 pm - 5:30 pm
Instructor(s): Gerald Benoît
Section 4
Tu 6:30 pm - 8:00 pm
Instructor(s): Ysis Wilson-Tarter
Section 5
We 4:00 pm - 5:30 pm
Instructor(s): Mumin Khan
Section 6
We 6:30 pm - 8:00 pm
Instructor(s): Ysis Wilson-Tarter
Section 8
Th 4:00 pm - 5:30 pm
Instructor(s): Gunnar Kleemann
Section 9
Th 6:30 pm - 8:00 pm
Instructor(s): Gunnar Kleemann
Section 98
Tu 4:00 pm - 5:30 pm
Instructor(s): Ysis Wilson-Tarter
This class is for 5th Year MIDS Students ONLY.
This class is for 5th Year MIDS Students ONLY.
Section 99
Th 2:00 pm - 3:30 pm
Instructor(s): Ysis Wilson-Tarter
This class is for 5th Year MIDS Students ONLY.
This class is for 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
Mo 6:30 pm - 8:00 pm
Instructor(s): Brooks Ambrose
Section 3
Mo 6:30 pm - 8:00 pm
Instructor(s): Carlos Rivera
Section 4
Tu 2:00 pm - 3:30 pm
Instructor(s): JP Dolphin
Section 5
We 2:00 pm - 3:30 pm
Instructor(s): Brooks Ambrose
Section 6
We 4:00 pm - 5:30 pm
Instructor(s): Napoleon Paxton
Section 7
We 6:30 pm - 8:00 pm
Instructor(s): Donna Dueker
Section 8
Th 4:00 pm - 5:30 pm
Instructor(s): Napoleon Paxton
Section 9
Th 6:30 pm - 8:00 pm
Instructor(s): Conor Healy
Section 10
Fr 6:30 pm - 8:00 pm
Instructor(s): Donna Dueker
Section 11
Sa 10:00 am - 11:30 am
Instructor(s): Elena Petrov
Section 12
Th 4:00 pm - 5:30 pm
Instructor(s): Conor Healy

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 97
Mo 2:00 pm - 3:30 pm, We 2:00 pm - 3:00 pm
Instructor(s): Michael Rivera
This class is for 5th Year MIDS Students ONLY.
This class is for 5th Year MIDS Students ONLY.
Section 98
Mo 4:00 pm - 5:30 pm, We 4:00 pm - 5:00 pm
Instructor(s): Brooks Ambrose
This class is for 5th Year MIDS Students ONLY.
This class is for 5th Year MIDS Students ONLY.
Section 99
Tu 6:30 pm - 8:00 pm, Th 6:30 pm - 7:30 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): Majid Maki
Section 2
Tu 6:30 pm - 8:00 pm
Instructor(s): Majid Maki
Section 3
Tu 4:00 pm - 5:30 pm
Instructor(s): Paul Laskowski
Section 4
We 6:30 pm - 8:00 pm
Instructor(s): Bill Chung
Section 5
We 6:30 pm - 8:00 pm
Instructor(s): Mark Labovitz
Section 6
We 4:00 pm - 5:30 pm
Instructor(s): Tanya Roosta
Section 7
Th 6:30 pm - 8:00 pm
Instructor(s): Bill Chung
Section 8
We 6:30 pm - 8:00 pm
Instructor(s): Gunnar Kleemann
Section 98
Tu 2:00 pm - 3:30 pm
Instructor(s): Paul Laskowski
This class is for 5th Year MIDS Students ONLY.
This class is for 5th Year MIDS Students ONLY.
Section 99
Th 4:00 pm - 5:30 pm
Instructor(s): Paul Laskowski
This class is for 5th Year MIDS Students ONLY.
This class is for 5th Year MIDS Students 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
Tu 4:00 pm - 5:30 pm
Instructor(s): Kevin Crook
Section 2
Tu 6:30 pm - 8:00 pm
Instructor(s): Kevin Crook
Section 3
Tu 6:30 pm - 8:00 pm
Instructor(s): Shiraz Chakraverty
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): Kevin Crook
Section 7
Th 6:30 pm - 8:00 pm
Instructor(s): Kevin Crook
Section 8
We 6:30 pm - 8:00 pm
Instructor(s): Shiraz Chakraverty
Section 98
Tu 2:00 pm - 3:30 pm
Instructor(s): Doris Schioberg
This class is for 5th Year MIDS Students ONLY.
This class is for 5th Year MIDS Students ONLY.
Section 99
Tu 4:00 pm - 5:30 pm
Instructor(s): Doris Schioberg
This class is for 5th Year MIDS Students ONLY.
This class is for 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
Tu 2:00 pm - 3:30 pm
Instructor(s): Amit Bhattacharyya
Section 2
Tu 4:00 pm - 5:30 pm
Instructor(s): John Santerre
Section 3
Tu 4:00 pm - 5:30 pm
Instructor(s): Nedelina Teneva
Section 4
Tu 6:30 pm - 8:00 pm
Instructor(s): Nedelina Teneva
Section 5
We 4:00 pm - 5:30 pm
Instructor(s): Cornelia Paulik
Section 6
We 6:30 pm - 8:00 pm
Instructor(s): Cornelia Paulik
Section 7
Th 4:00 pm - 5:30 pm
Instructor(s): Ishaani Priyadarshini
Section 8
Th 6:30 pm - 8:00 pm
Instructor(s): Ishaani Priyadarshini
Section 9
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): Andy Reagan
Section 2
Mo 6:30 pm - 8:00 pm
Instructor(s): Mak Ahmad
Section 3
Tu 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): Fereshteh Amini
Section 6
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 J. Shen, Todd Holloway
Section 2
Tu 4:00 pm - 5:30 pm
Instructor(s): Puya H. Vahabi, Korin Reid
Section 3
Tu 4:00 pm - 5:30 pm
Instructor(s): Fred Nugen, Ramesh Sarukkai
Section 4
Tu 4:00 pm - 5:30 pm
Instructor(s): Uri Schonfeld, D. Alex Hughes
Section 5
Tu 6:30 pm - 8:00 pm
Instructor(s): Fred Nugen, Korin Reid
Section 6
Tu 6:30 pm - 8:00 pm
Section 7
Th 2:00 pm - 3:30 pm
Instructor(s): Cornelia Paulik, Zona Kostic
Section 8
Th 4:00 pm - 5:30 pm
Instructor(s): Joyce J. Shen, Kira Wetzel
Section 9
Th 6:30 pm - 8:00 pm
Instructor(s): Puya H. Vahabi, Daniel Aranki
Section 10
Th 6:30 pm - 8:00 pm
Instructor(s): Fred Nugen, Danielle Cummings

In the Capstone class, students combine technical, analytical, interpretive, problem-solving, and strategic thinking dimensions to design and execute a full end-to-end data science project. Students will develop their technical and non-technical skills as data scientists who focus on real-world and impactful applications and situations. The final project provides a learning opportunity and “sandbox” to integrate all skills and concepts learned throughout the MIDS program and provides experience and hands-on tools in formulating and implementing an impactful and compelling project. Students are evaluated on their ability to work in a dynamic team environment to collaborate, co-develop, and communicate their work.

Section 98
Mo 4:00 pm - 5:30 pm, We 4:00 pm - 5:00 pm
Instructor(s): Joyce J. Shen, D. Alex Hughes
This class is for 5th Year MIDS Students ONLY.
This class is for 5th Year MIDS Students ONLY.
Section 99
Mo 6:30 pm - 8:00 pm, We 6:30 pm - 7:30 pm
Instructor(s): Joyce J. Shen, Kevin Hartman
This class is for 5th Year MIDS Students ONLY.
This class is for 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 4:00 pm - 5:30 pm
Instructor(s): Deb Donig, Jared Maslin
Section 2
Tu 6:30 pm - 8:00 pm
Instructor(s): Deb Donig, Jared Maslin
Section 3
Th 4:00 pm - 5:30 pm
Instructor(s): Deb Donig, Jared Maslin

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

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 4:00 pm - 5:30 pm
Instructor(s): Stephen Muchovej
Section 2
Tu 6:30 pm - 8:00 pm
Instructor(s): James York-Winegar
Section 3
We 6:30 pm - 8:00 pm
Instructor(s): Luis Villarreal
Section 4
We 6:30 pm - 8:00 pm
Instructor(s): Amanda Ford
Section 5
Sa 8:00 am - 9:30 am
Instructor(s): James York-Winegar

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 6:30 pm - 8:00 pm
Instructor(s): Siinn Che
Section 2
We 4:00 pm - 5:30 pm
Instructor(s): Vinicio De Sola
Section 3
We 6:30 pm - 8:00 pm
Instructor(s): Vinicio De Sola
Section 4
Th 4:00 pm - 5:30 pm
Instructor(s): Vinicio De Sola
Section 5
Th 6:30 pm - 8:00 pm
Instructor(s): Vinicio De Sola
Section 6
Tu 6:30 pm - 8:00 pm
Instructor(s): Ramakrishna Gummadi
Section 7
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): Jennifer Zhu
Section 2
Tu 2:00 pm - 3:30 pm
Instructor(s): Peter Grabowski
Section 3
We 4:00 pm - 5:30 pm
Instructor(s): Natalie Ahn
Section 4
We 6:30 pm - 8:00 pm
Instructor(s): Paul Spiegelhalter, Mike Tamir
Section 5
Th 4:00 pm - 5:30 pm
Instructor(s): Mark Butler
Section 6
Th 6:30 pm - 8:00 pm
Instructor(s): Mark Butler
Section 7
Th 6:30 pm - 8:00 pm
Instructor(s): Jennifer Zhu

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
Mo 6:30 pm - 8:00 pm
Instructor(s): Majid Maki
Section 2
Tu 4:00 pm - 5:30 pm
Instructor(s): Vinod Bakthavachalam
Section 3
Th 6:30 pm - 8:00 pm
Instructor(s): Mark Labovitz

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
Tu 4:00 pm - 5:30 pm
Instructor(s): Rachel Brown
Section 2
Th 4:00 pm - 5:30 pm
Instructor(s): Rachel Brown
Section 3
Th 6:30 pm - 8:00 pm
Instructor(s): Rachel Brown

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, business and enterprise technology strategy, business and data business model, market analysis, leadership into one class. Students are required to participate in weekly sessions and write response pieces as well as a final paper and presentation studying one defining data application or emerging technology in ML/AI 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 current data application or emerging applications in various industries. This course will include a leadership module to cultivate long-term strategic, management, and analytical insights and capabilities.  Equally important, this course trains students who aspire to be data science leaders who can effectively communicate, collaborate, innovate alongside their business and non-technical stakeholders in any organizational environment. This is the course for you if you want to develop a deep understanding on both the technical side and the applied side in various contemporary and emerging machine learning and artificial intelligence 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 J. Shen
Pre-requisites: MIDS Students Only. DATASCI 205:... more
Pre-requisites: MIDS Students Only. DATASCI 205: Fundamentals of Data Engineering or DATASCI 207: Applied Machine Learning; Can be taken simultaneously with DATASCI 205 and DATASCI 207; Cannot be taken simultaneously with DATASCI 210. 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.