Offered through: Information
Terms offered: Fall 2017, Summer 2017, Spring 2017
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.

Offered through: Information
Terms offered: Fall 2017, Summer 2017, Spring 2017
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. Course must be taken for a letter grade to fulfill degree requirements.

Offered through: Information
Terms offered: Fall 2017, Summer 2017, Spring 2017
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.

Offered through: Information
Terms offered: Fall 2017, Summer 2017, Spring 2017
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.

Offered through: Information
Terms offered: Fall 2017, Summer 2017, Spring 2017
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.

Offered through: Information
Terms offered: Fall 2017, Summer 2017, Spring 2017
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 as well as 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.

Offered through: Information
Terms offered: Fall 2017, Summer 2017, Spring 2017
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.

Offered through: Information
Terms offered: Fall 2017, Summer 2017, Spring 2017
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.

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Terms offered: Fall 2017, Summer 2017, Spring 2017
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.

Offered through: Information
Terms offered: Fall 2017, Summer 2017, Spring 2017
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.

Offered through: Information
Terms offered: Fall 2017, Spring 2017, Fall 2016
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.

Offered through: Information
Terms offered: Fall 2017, Summer 2017, Spring 2017
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.