Course TitleCourse numberDays and TimesDescriptionInstructorUnits

Foundations of Data Science

CS/ INFO/ STAT c8 MWF 10-11

This introductory course in data science is built on three interrelated perspectives: inferential thinking, computational thinking, and real-world relevance.

John DeNero 4

Computational Structures in Data Science

CS 88 Mon 4-5 pm

Introduction to computer science in the context of data science. This course provides a formal and rigorous introduction to the programming topics that appear in Foundations of Data Science, expands the repertoire of computational concepts, and exposes students to techniques of abstraction at several levels, including layers of software and machines from a programmers’ point of view.

David Culler 2

Data and Ethics

INFO 88 Tu 330-530 PM

This course provides an introduction to critical and ethical issues surrounding data and society. It blends social and historical perspectives on data with ethics, policy, and case examples. 

Anna Lauren Hoffmann 2

Data Science and the Mind

CogSCI 88 M 12-2 pm

How does the human mind work? We explore this question by analyzing a range of data concerning such topics as human rationality and irrationality, human memory, how objects and events are represented in the mind, and the relation of language and cognition. This class provides young scientists with critical thinking and computing skills that will allow them to work with data in cognitive science and related disciplines.

Yang Xu 2

Data Science for Smart Cities

CEE 88 Tu 9:00-11:00 am

This course provides an introduction to working with data generated within transportation systems, power grids, communication networks, as well as collected via crowd-sensing and remote sensing technologies, to build demand- and supply-side urban services based on data analytics.

Alexey Pozdnukhov 2

Data Sciences in Ecology and the Environment

ESPM 88B Tues 11:00-1:00

In this course students will apply methods learned in the Foundations course to explore, pose, and answer key questions using relevant data from the Ecological and Environmental Sciences.

Carl Boettiger 2

Exploring Geospatial Data

ESPM 88A M 4:00-6:00 pm

This course provides an introduction to working with digital geographic data, or geospatial data. We will explore concepts of geospatial data representation, methods for acquisition, processing and analysis, and techniques for creating compelling geovisualizations.

Patty Frontiera 2

Health, Human Behavior, and Data

L&S 88-1 M 1-3 pm

We will examine and discuss measures of human health and longevity alongside arrays of measurable influences on health, identifying the key questions traditionally addressed in health sciences and exploring the current frontier. We will develop broad knowledge of the metrics, methods, and challenges, and we will apply them toward understanding of current issues in health policy.

Ryan Edwards 2

How Does History Count?

HIST 88 Tu 2:00-4:00 PM

The course addresses how workable historical data are constructed out of incomplete, selective, and messy primary materials, such as archival collections, textual materials, or administrative records.

Andrej Milivojevic 2

Introduction to Matrices and Graphs in Data Science

STAT 89A Mon 1:00 - 3:00

This connector will cover introductory topics in the mathematics of data science, focusing on discrete probability and linear algebra and the connections between them that are useful in modern theory and practice.

Michael Mahoney 2

Literature and Data

L&S 88-2 Tu 4:00-6:00 PM

In this course, we will apply methods learned in Foundations of Data Science to sets of literary texts in order to expand our reading practices. This humanities-oriented approach will require us to think about the limits of both new and traditional reading methods and how we make arguments based on data.

Teddy Roland 2

Probability and Mathematical Statistics in Data Science

STAT 88 Tu 4-6 PM

Topics include: total variation distance between discrete distributions; the mean, standard deviation, and tail bounds; correlation, and the derivation of the regression equation; probabilities, random variables, and the Central Limit Theorem; probabilistic models; symmetries in random permutations; prior and posterior distributions, and Bayes’ rule.

Ani Adhikari 2

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