Connector courses weave together core concepts and approaches from Data 8 with complementary ideas or areas. Along the way, students gain additional experience, broader insights, or deeper theoretical or computational foundations.
Offered by faculty across many departments and fields of study, connectors are optional but highly encouraged and are designed to be taken at the same time or after the Foundations course. They offer two or more units of course credit. Data 8 and connectors complement each other and often use similar materials or tools.
For connector courses being offered in the current/next semester, please view our current course offerings.
For current and future connector instructors who are interested in learning more about the technology and workflows behind the program, please visit the Connector Guide(link is external).
Connector Courses from 2015 - Present
Below is a list of all connector courses that have been offered since Fall 2015.
| Title | Course number | Description |
|---|---|---|
| Data Science for Smart Cities | CIV ENG 88 | Design and operation of smart, efficient, and resilient cities nowadays require data science skills. 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. |
| Time Series Analysis: Sea Level Rise and Coastal Flooding | CIV ENG 88B | In this course, we will pursue analysis of long-term records of coastal water levels in the context of sea level rise. We will cover the collection, evaluation, visualization and analysis of time series data using long-term records of sea levels from coastal sites around the world. |
| Data Science and the Mind | COGSCI 88 | 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. |
| Computational Structures in Data Science | COMPSCI 88 | 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. |
| Economic Models(link is external) | DATA 88E | This Data Science connector course will motivate and illustrate key concepts in Economics with examples in Python Jupyter notebooks. The course will give data science students a pathway to apply python programming and data science concepts within the discipline of economics. The course will also give economics students a pathway to apply programming to reinforce fundamental concepts and to advance the level of study in upper division coursework and possible thesis work. |
| Data Science for Genetics and Genomics(link is external) | DATA 88 | Recent years have witnessed a rapid expansion in the creation and utilization of genetic and genomic data across diverse domains such as business, biological research, and medicine. In this Data 8 connector course we will survey relevant questions of interest and employ the methods frequently relied upon by analysts to derive insights from genetic and genomic data. Topics will include the comparison of DNA sequences, dimension reduction, the characterization of transcriptomes, and genome-wide association studies, among others. In addition to hands-on work with data, we will also consider the history of the genetic and genomic sciences and their intersection with current events, ethics, and modern medicine. Students should exit with an understanding of the central role played by data in the fields and an appreciation for the remaining challenges in light of ever-increasing degrees of personalization of, and access to, these sciences. No biological background is required. |
| Writing Data Stories | Data 88 | Communication is a critical yet often overlooked part of data science. This course aims to help you learn how to write about data insights in a way that is both compelling and faithful to the data. You will gain an “ear” for writing by reading the work of others. You will learn how to distill findings into an accessible story, and organize and revise the story. You will also gain experience writing clearly, concisely, and precisely for broad audiences. Exercises include: writing captions for figures, data descriptions, and annotations for storyboards; editing statistical statements for simplicity and accuracy; and drafting and revising short data stories. |
| Broken Down by Age and Sex: Data Science and Demography | Data 88 | Demography is the science of populations and how they change—including death, sex, migration, marriage, and more. Today, demography is a critical part of answering the most pressing questions that face populations all over the world: who is most vulnerable to the coronavirus pandemic? Why do some countries become rich, while some remain poor? Which forces guide the shifting landscape of politics and voting? In this connector, we will take a tour of cutting-edge problems in demography and how data science can be used to help address them. |
| Language Models, Text Analysis | DATA 88 | No description available. |
| Sports Analytics | DATA 88 | Sports Data Analytics is a connector course to Data8 and will follow Data8's technical curriculum with specific examples from and applications to analyzing the rich world of sports data. We will primarily use publicly available data from sports such as Major League Baseball and NBA basketball, but students will be encouraged to explore and analyze data from other sports. We will address questions around data acquisition, performance measurement, and real-world uses of analytics. Prerequisites: Concurrent registration in Data8 or knowledge of equivalent material. |
| Data Science & Immigration | DEMOG 88 | This course will cover the small but important part of the rich history of human migration that deals with the population of the United States--focusing on the period between 1850 and the present. |
| Exploring Geospatial Data | ESPM 88A | From interactive web maps to spatial data analysis, digital geographic data and information are becoming an important part of the data science landscape. Almost everything happens somewhere that can be mapped on the surface of the earth. |
| Data Sciences in Ecology and the Environment | ESPM 88B | 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. |
| Data Science Applications in Geography | GEOG 88 | Data science methods are increasingly important in geography and earth science. This course introduces some of the particular challenges of working with spatial data arising from characteristics specific to such data. |
| How Does History Count? | HIST 88 | We will explore how historical data becomes historical evidence and how recent technological advances affect long-established practices, such as close attention to historical context and contingency. Will the advent of fast computing and big data make history “count” more or lead to unprecedented insights into the study of change over time? |
| How Does History Count? Exploring Japanese-American Internment through Digital Sources | HIST 88 | On February 19, 1942, Executive Order 9066 authorized the detention of more than 120,000 people of Japanese descent, most of them American citizens living on the west coast. In this data science connector course, students will learn emerging digital methods for conducting historical research, which they will apply to the study of Japanese-American Internment. |
| How does History Count? Enumerating Nature: Data Science and Environmental History | HIST 88 | In an era of environmental uncertainty, we must address a critical question – how did we get to this point? Historians have wontedly depicted the natural world as a muted figure with an elusive past. Deciphering nature’s complex legacy requires the employment of materials and methods outside of the traditional purview of historians. However, we are no longer bound by traditional historical evidence. Data Science facilitates us with innovative tools and methods to examine quantitative sources. In this connector course, we will explore how historical data becomes historical evidence and how recent technological advances affect long-established practices, such as close attention to historical context and contingency. Will the advent of fast computing and big data make history “count” more or lead to unprecedented insights into the study of change over time? During our weekly discussions, we will apply what we learn in lectures and labs to the analysis of selected historical sources and get an understanding of constructing historical datasets. We will also consider scholarly debates over quantitative evidence and historical argument. |
| Data and Ethics | INFO 88A | This course provides an introduction to critical and ethical issues surrounding data and society. |
| Crime and Punishment: Taking the Measure of the US Justice System | LEGAL ST 88 | We will explore how data are used in the criminal justice system by exploring the debates surrounding mass incarceration and evaluating a number of different data sources that bear on police practices, incarceration, and criminal justice reform. Students will be required to think critically about the debates regarding criminal justice in the US and to work with various public data sets to assess the extent to which these data confirm or deny specific policy narratives. Building on skills from Foundations of Data Science, students will be required to use basic data management skills working in Python: data cleaning, aggregation, merging and appending data sets, collapsing variables, summarizing findings, and presenting data visualizations. |
| Race, Policing, and Data Science | L&S 39D | In this class, we will review available data sources on race and policing and ask what those data have to say about current events and the types of claims, typically causal, commonly invoked in public discourse surrounding these issues. |
| Social Networks | L&S 88 | Insights from the study of social networks are used in a wide range of real-world settings, from predicting and preventing the spread of Ebola, to convincing people to vote for a political candidate, to connecting people across the globe through Facebook. Learn how to work with social network data and why it’s useful. |
| Data Science for Cognitive Neuroscience | L&S 88 | The human brain is a complex information processing system and is currently the topic of multiple fascinating branches of research. Understanding how it works is a very challenging scientific task. |
| Health, Human Behavior, and Data | L&S 88 | 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. |
| Child Development Around the World: Analyzing Household Data Sets | L&S 88 | How can we improve children’s health and learning in the developing world? Students will use the World Bank’s household survey data to explore relationships between nutrition and education outcomes and a variety of socioeconomic variables. |
| Genomics and Data Science | L&S 88 | Genomics is triggering a revolution in medical discovery. Students will explore genomic data, including HIV genomics, personal genomics, and DNA forensics, as well as related legal and ethical issues. Biology background not required. |
| Literature and Data | L&S 88 | 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. |
| Web Data Visualization | L&S 88 |
The course introduces students to Web science with a focus on how the Web works, types of Web data, and how to generate effective visualizations for Web data (e.g., social networks). The course covers basic principles and tools for understanding and visualizing Web data. It focuses heavily on project work that aims to give students hands-on experience with handling Web data. Because Web science is interdisciplinary by nature, this course connects students to different disciplines such as social science, computer science, and information science. At the end of the course, students should be able to apply Web mining techniques on and draw insights from real world data. |
| Behind the Curtain in Economic Development |
L&S 88 |
This class will give students hands on experience looking at how data is gathered for analysis and provide context for the use of data for studying applied issues in economic development. The background will be a set of policy questions from a field study in rural Kenya on child health and clean drinking water. |
| Rediscovering Texts as Data | L&S 88 |
Humanists have traditionally emphasized the ‘close reading’ of a text, where value is placed on the nuances of specific passages. The increasing amount of digital text being published and archived affords us an opportunity to read text differently—as data on a scale larger than ever before. This ‘distant reading’ approach (mediated through the computer) complements our ‘close reading’ by providing a broader context for interpretation previously inaccessible. |
| Crime and Punishment: Taking the Measure of the US Justice System | LEGALST 88 | We will explore how data are used in the criminal justice system by exploring the debates surrounding mass incarceration and evaluating a number of different data sources that bear on police practices, incarceration, and criminal justice reform. |
| Immunotherapy of Cancer: Success and Failure | MCB 88 | We will work with a variety of datasets that describe a molecular view of cells and how they divide. We will learn about the processes that cause cells to become specialized (differentiate) and to give rise to cancer (transform). |
| Data Science Applications in Physics | PHYSICS 88 | Introduction to data science with applications to physics. Topics include: statistics and probability in physics, modeling of the physical systems and data, numerical integration and differentiation, function approximation. Connector course for Data Science 8, room-shared with Physics 77. Recommended for freshmen intended to major in physics or engineering with emphasis on data science. |
| Data Science for Social Impact | SOCIOL 88 | This course explores the role of social research in policymaking and public decisions and develops skills for the communication of research findings and their implications in writing and through data visualization. Students will develop an understanding of various perspectives on the role that data and data analysts play in policymaking, learn how to write for a public audience about data, results, and implications, and learn how to create effective and engaging data visualizations. |
| Probability and Mathematical Statistics in Data Science | STAT 88 | In this connector course we will state precisely and prove results discovered in the foundational data science course through working with data. |
| Introduction to Matrices and Graphs in Data Science | STAT 89A | 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. |
| Data and Decisions | UGBA 88 | The goal of this connector course is to provide an understanding of how data and statistical analysis can improve managerial decision-making. We will explore statistical methods for gleaning insights from economic and social data, with an emphasis on approaches to identifying causal relationships. We will discuss how to design and analyze randomized experiments and introduce econometric methods for estimating causal effects in non-experimental data. The course draws on a variety of business and social science applications, including advertising, management, online marketplaces, labor markets, and education. This course, in combination with the Data 8 Foundations course, satisfies the statistics prerequisite for admission to Haas. |