This online course is designed to help anyone teach – and learn – with a 21st century approach to knowledge and teaching. Lesson 1 of the course shares important evidence we now have about the working of the brain, that is meaningful for all subjects and ages – and lives. We then move to thinking together about the data filled world in which we live, to prepare students for their future in a world of data.
The aim of a data science approach is not to add new standards or content to your teaching, it is about interacting with your content in a data science way – that is fun, interesting and creative. In the course you will experience lessons that you can take and use with your students, and you will see lots of classroom and lesson examples. Whether you are a kindergarten teacher, a high school history or maths teacher, an administrator or parent, or someone just curious about data science, there will be ideas for you.

This is a research-oriented course on algorithm engineering, which will cover both the theory and practice of algorithms and data structures. Students will learn about models of computation, algorithm design and analysis, and performance engineering of algorithm implementations. We will study the design and implementation of sequential, parallel, cache-efficient, external-memory, and write-efficient algorithms for fundamental problems in computing. Many of the principles of algorithm engineering will be illustrated in the context of parallel algorithms and graph problems.

This course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion of 6.034, students should be able to develop intelligent systems by assembling solutions to concrete computational problems; understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering; and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.

Modeling traffic data is important for urban planning, creating transportation systems, and even predicting how much foot traffic a retail store can expect in a given day. This genre of dynamic data science activities could be classified as “finding a needle in a haystack,” giving students a chance to mine big data to make insights about traffic use.

According to the Bay Area Rapid Transit District, about 400,000 people used the BART system daily in 2018. In BARTy, students investigate BART data from 2015 to learn about passenger use and explore traffic patterns. The Teacher Guide includes a game-like investigation to locate a “mystery meeting,” and suggests ways to help students figure out peak passenger use, popular stations, and the impact of events in San Francisco on BART usage.

This book provides students with an introduction to the skills needed to succeed in Data Science.

The MIT Case Studies in Social and Ethical Responsibilities of Computing (SERC) aims to advance new efforts within and beyond MIT’s Stephen A. Schwarzman College of Computing. The specially commissioned and peer-reviewed cases are brief and intended to be effective for undergraduate instruction across a range of classes and fields of study. The series editors expect the cases will also be of interest for computing professionals, policy specialists, and general readers. All cases will be made freely available via open-access publishing, with author retained copyright, through Creative Commons licensing.
The Series Editors interpret “social and ethical responsibilities of computing” broadly. Some cases focus closely on particular technologies, others on trends across technological platforms. Still others examine social, historical, philosophical, legal, and cultural facets that are essential for thinking critically about present-day efforts in computing and data sciences.

The goal of the Climate Primer website is to summarize the most important lines of evidence for human-caused climate change. It confronts the stickier questions about uncertainty in our projections, engages in a discussion of risk and risk managment, and concludes by presenting different options for taking action. We hope that the facts prepare you for more effective conversations with your community about values, trade-offs, politics, and actions.
In March 2024, the MIT Environmental Solutions Initiative launched the first major update to the Climate Primer. The updated Primer includes more precise estimates of future global warming and its effects on global temperatures and extreme weather events, important advances in climate modeling, new actions taken around the world to adapt to the impacts of climate change, and the latest data about the pace at which clean energy and other critical climate solutions are being deployed. Read more about the update on the MIT Environmental Solutions website.

This course provides an introductory survey of data science tools in healthcare. It was created by members of MIT Critical Data, a global consortium consisting of healthcare practitioners, computer scientists, and engineers from academia, industry, and government, that seeks to place data and research at the front and center of healthcare operations.
The most daunting global health issues right now are the result of interconnected crises. In this course, we highlight the importance of a multidisciplinary approach to health data science. It is intended for front-line clinicians and public health practitioners, as well as computer scientists, engineers, and social scientists, whose goal is to understand health and disease better using digital data captured in the process of care.
What you’ll learn:

Principles of data science as applied to health
Analysis of electronic health records
Artificial intelligence and machine learning in healthcare

This course is part of the Open Learning Library, which is free to use. You have the option to sign up and enroll in the course if you want to track your progress, or you can view and use all the materials without enrolling.

CODAP (Common Online Data Analysis Platform) is an easy to use data analysis environment that can be used in a wide variety of educational settings. CODAP is designed for grades 5 through 14, and aimed at teachers and curriculum developers. CODAP can be used across the curriculum to help students summarize, visualize, and interpret data, Conadvancing their skills to use data as evidence to support a claim.

A perfect introduction to the exploding field of Data Science for the curious, first-time student. The author brings his trademark conversational tone to the important pillars of the discipline: exploratory data analysis, choices for structuring data, causality, machine learning principles, and introductory Python programming using open-source Jupyter Notebooks. This engaging read will allow any dedicated learner to build the skills necessary to contribute to the Data Science revolution, regardless of background.

CODAP (Common Online Data Analysis Platform) is an open-source data visualization and analysis tool made available by the Concord Consortium. It's available at https://codap.concord.org/. CODAP can be used across the curriculum to help students summarize, visualize, and interpret data, advancing their skills to use data as evidence to support a claim.

This professional learning resource includes guides to get started, tutorials that demonstrate the features and functionality of CODAP, sample lessons, and links to online forum sites.

This is a textbook for teaching data science using the Python programming language at a general first year undergraduate level.

This is a textbook for teaching data science using the R programming language at a general first year undergraduate level.

This page shares five units of youcubed lessons for grades 6-10 that introduce students (and teachers) to data science. The units start with an introduction to the concept of data and move to lessons that invite students to explore their own data sets. These lessons teach important content through a pattern-seeking, exploratory approach, and are designed to engage students actively.







Data Science LessonsThis page shares five units of youcubed lessons for grades 6-10 that introduce students (and teachers) to data science. The units start with an introduction to the concept of data and move to lessons that invite students to explore their own data sets.  These lessons teach important content through a pattern-seeking, exploratory approach, and are designed to engage students actively. The culminating unit is a citizen science project that gives students an opportunity to conduct a data inquiry. The lessons accompany a new online course for teachers, where some of the lessons are featured, along with other lesson ideas. These lessons are offered with ideas for in-person or online teaching, and can be taught at any time of year.









LessonsTeacher Online Course: 21st Century Teaching and LearningUnit 1: Data Is EverywhereUnit 2: Working With Data Analysis ToolsUnit 3: Measures of Center & SpreadUnit 4: Understanding VariabilityUnit 5: A Community Data Collection Project

ResourcesHigh School Data Science CourseCODAPWhat's Going On In This Graph?Data Science Initiative VideoThe Data Science K-12 MovementData Talks



Cool Extras




What are Data Talks?






A Picture Book Introduction to Data Science






Measures of Center and Spread Animated Movies






Stanford News Press Release

Data Science and AI in Psychology is an interactive eTextbook that provides an introduction to data science, big data, and machine learning in psychology. It covers current trends in data science and big data in the field of psychology (Chapter 1), applications of AI in the field of psychology (Chapter 2), the psychology of data visualization (Chapter 3), data ethics (Chapter 4), an introduction to how machines learn (Chapter 5), a hands-on guide for reading and critiquing machine learning research articles that are relevant to psychological topics (Chapters 6 and 7), and an introduction to coding in Python (Chapter 8). This eTextbook also includes an introduction to ChatGPT and tips for using ChatGPT to assist with writing and coding without plagiarizing (Chapters 6 and 8). This is an interactive resource that provides students with opportunities to engage with their peers and develop critical thinking skills through problem-based, active learning.

The goal of the Data4Kids project is to help educators prepare children to be better data users, stewards, and consumers. With support from the South Big Data Hub, the Urban Institute and its partners have created a set of tools and resources to help teach kids in primary and secondary schools about data, data science, and data visualization in a virtual environment.

These "Data Stories" are designed to assist educators in supporting students’ data science learning, and can be allow educators to freely used across a variety of grades. Each story is a starter kit for educators at different levels–grades 3-5 (Band 1); grades 6-8 (Band 2); or grades 9-12 (Band 3).

Each Data Story includes an Instructor's Guide, Data (available in Microsoft Excel, CSV, and Google Sheets formats), a Data Dictionary to describe the data values in each story (available in Microsoft Word and Google Doc formats), and Teaching Slides (available in Microsoft PowerPoint and Google Slides formats).

Data talks are short 5-10 minute classroom discussions to help students develop data literacy. This pedagogical strategy is similar in structure to a number talk, but instead of numbers students are shown a data visual and asked what interests them.

This course relies on primary readings from the database community to introduce graduate/undergraduate students to the foundations of database systems, focusing on basics such as the relational algebra and data model, schema normalization, query optimization, transactions, and other more advanced topics. No prior database experience is assumed, though students who have taken an undergraduate course in databases are encouraged to attend.

This resource is to support teachers and educators to run Day of AI activities in their classrooms through curriculum packages and teacher training, all of which is available at no cost to participants.
Developed by leading faculty and educators from MIT RAISE, the curriculum features up to four hours of hands-on activities that engage kids in creative discovery, discussion, and play as they learn the fundamentals of AI, investigate the societal impact of these technologies, and bring artificial intelligence to life through lessons and activities that are accessible to all, even those with no computer science or technical background.

Digital Scholarship and Data Science Essentials for Library Professionals is an open and collaboratively curated training reference resource. It aims to make it easier for LIBER library professionals to gain a concise overview of the new technologies that underpin digital scholarship and data science practice in research libraries today, and find trusted training materials recommendations to start their professional learning journey.