Data Science Course Schedule summer 2017

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.

Student Learning Outcomes

  • Be able to design, reason about, and implement algorithms for solving computational problems.
  • Be able to generate an exploratory analysis of a data set using Python.
  • Be able to navigate a file system, manipulate files, and execute programs using a command line interface.
  • Be able to test and effectively debug programs.
  • Be fluent in Python syntax and familiar with foundational Python object types.
  • Be prepared for further programming challenges in more advanced data science courses.
  • Know how to read, manipulate, describe, and visualize data using the Numpy and Pandas packages.
  • Know how to use Python to extract data from different type of files and other sources.
  • Understand how to manage different versions of a project using Git and how to collaborate with others using Github.
  • Understand the principles of functional programming.
  • Understand the principles of object-oriented design and the process by which large pieces of software are developed.
Section: 1
Tu 4:00-5:30PM Pacific
Instructor(s): Christopher Llop
Section: 2
Th 4:00-5:30PM Pacific
Instructor(s): Gunnar Kleemann
Section: 3
Th 6:30-8:00PM Pacific
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.

Course must be taken for a letter grade to fulfill degree requirements.

Section: 1
M 4:00-5:30PM
Instructor(s): Brooks Ambrose
Section: 2
M 6:30-8:00PM
Instructor(s): Brooks Ambrose
Section: 3
Tu 4:00-5:30PM Pacific
Instructor(s): Yoonjung Lee
Section: 4
Tu 6:30-8:00PM Pacific
Instructor(s): Yoonjung Lee
Section: 5
W 4:00-5:30PM Pacific
Instructor(s): Charles Gomez
Section: 6
W 6:30-8:00PM Pacific
Instructor(s): Charles Gomez

The goal of this course is to provide students with 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.

(Prior to Fall 2016, the course was titled “Exploring and Analyzing Data.”)

Course must be taken for a letter grade to fulfill degree requirements.

Section: 1
M 4:00-5:30PM Pacific
Instructor(s): Jennifer Shin
Section: 2
M 6:30-8:00PM Pacific
Instructor(s): Jennifer Shin
Section: 3
Tu 4:00-5:30PM Pacific
Instructor(s): Jeff Yau
Section: 4
Tu 6:30-8:00PM Pacific
Instructor(s): Jeff Yau
Section: 5
Th 4:00-5:30PM
Instructor(s): Ryan Kappedal
Section: 6
Th 6:30-8:00PM
Instructor(s): Ryan Kappedal

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.

Course must be taken for a letter grade to fulfill degree requirements.

Section: 2
Tu 4:00-5:30PM Pacific
Instructor(s): Edward Fine
Section: 3
Tu 6:30-8:00PM Pacific
Instructor(s): Edward Fine
Section: 4
W 4:00-5:30PM Pacific
Instructor(s): Amit Bhattacharyya
Section: 5
Th 4:00-5:30PM Pacific
Instructor(s): Kevin Crook
Section: 6
Th 6:30-8:00PM Pacific
Instructor(s): Kevin Crook

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.

Course must be taken for a letter grade to fulfill degree requirements.

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
Tu 4:00-5:30PM Pacific
Instructor(s): Isabell Konrad
Section: 4
Tu 6:30-8:00PM Pacific
Instructor(s): Isabell Konrad
Section: 5
W 4:00-5:30PM Pacific
Instructor(s): Zachary Alexander
Section: 6
F 6:30-8:00PM Pacific
Instructor(s): Todd Holloway

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.

Course must be taken for a letter grade to fulfill degree requirements.

Section: 1
M 4:00-5:30PM Pacific
Instructor(s): Andy Reagan
Section: 2
W 4:00-5:30PM Pacific
Section: 3
Th 4:00-5:30PM Pacific
Instructor(s): John Alexis Guerra Gómez
Section: 4
Th 6:30-8:00PM Pacific
Instructor(s): John Alexis Guerra Gómez

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..

Course must be taken for a letter grade to fulfill degree requirements.

Section: 1
Tu 4:00-5:30PM Pacific
Instructor(s): Coco Krumme, David Steier
Section: 2
Tu 6:30-8:00PM Pacific
Instructor(s): Coco Krumme, David Steier
Section: 3
W 4:00-5:30PM Pacific
Instructor(s): Alberto Todeschini, Joyce Shen

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
Th 6:30-8:00PM Pacific
Section: 2
Th 4:00-5:30PM Pacific
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 Pacific
Instructor(s): D. Alex Hughes
Section: 2
Th 6:30-8:00PM Pacific
Instructor(s): D. Alex Hughes
Section: 3
W 4:00-5:30PM Pacific
Instructor(s): D. Alex Hughes

An overview of the contemporary toolkits for problems related to cloud computing and big data. Because the class is an advanced course, we generally assume familiarity with the concepts and spend more time on the implementation. Every lecture is followed by a hands-on assignment, where students get to experience some of the technologies covered in the lecture. By the time students complete the course, they should be able to name the big data problem they are facing, select proper tooling, and know enough to start applying it.

Section: 1
M 4:00-5:30PM Pacific
Section: 2
Tu 4:00-5:30PM Pacific
Instructor(s): Brad DesAulniers, 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
W 4:00-5:30PM Pacific
Instructor(s): Kyle Hamilton, Mike Tamir
Section: 2
W 6:30-8:00PM Pacific
Instructor(s): Mike Tamir

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
M 4:00-5:30PM Pacific
Instructor(s): James Kunz
Section: 2
Th 6:30-8:00PM Pacific
Instructor(s): Arathi Mani
Section: 3
F 4:00-5:30PM Pacific
Instructor(s): James Kunz

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.

Due to the intensive mathematical nature of Data Science W271, students are strongly encouraged to preview the class before registering. Request access to the course preview.

Section: 1
Tu 6:30-8:00PM Pacific
Instructor(s): Devesh Tiwari
Section: 2
W 6:30-8:00PM Pacific
Instructor(s): Devesh Tiwari