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.

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.

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

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.

Midterm Exam Review for the course "CSC 59970 – Intro to Data Science" delivered at the City College of New York in Spring 2019 by Grant Long as part of the Tech-in-Residence Corps program.

Midterm Exam and Answer Key for the course "CSC 59970 – Intro to Data Science" delivered at the City College of New York in Spring 2019 by Grant Long as part of the Tech-in-Residence Corps program.

Midterm Exam Review for the course "CS 217 – Probability and Statistics for Computer Science" delivered at the City College of New York in Spring 2019 by Evan Agovino as part of the Tech-in-Residence Corps program.

Practice Final Exam for the course "CS 217 – Probability and Statistics for Computer Science" delivered at the City College of New York in Spring 2019 by Evan Agovino as part of the Tech-in-Residence Corps program.

This course provides a comprehensive review of interoperability, health data standards, and other advanced topics including Substitutable Medical Applications, Reusable Technologies (SMART) and Fast Healthcare Interoperability Resources® (FHIR), also known as SMART-on-FHIR applications and Accelerator projects. This course will use Interoperability Land™ to provide learners with a hands-on experience using FHIR resources. Upon successful completion of this course, learners will be able to: explain interoperability and use cases; locate information within JSON and XML files; Create queries in IOL; understand SMART application authorization. Interoperability Land can be purchased on AWS Marketplace at the followinglink: https://aws.amazon.com/marketplace/pp/prodview-f34r2uj3naohe For information regarding education pricing please email contactus@interoperabilityinstitute.org.

In this dynamic data science game, students try to track down a speck of extremely dangerous radioactive material (the "source"), which has been lost somewhere in the middle of their lab. A special device measures the strength of the radiation and, if it’s positioned correctly over the speck, can be used to collect it for safe disposal. But it's a tiny speck, so they have to give quite precise coordinates. They take measurements to figure out the speck’s location, but must beware: as they take measurements, they're also accumulating radiation exposure. If they get too much, they’ll lose the game and will have to start over. Can they find the source before it’s too late? Using mathematical models, students generate useful strategies for winning the game with data.

Lecture for the course "CSC 59970 – Intro to Data Science" delivered at the City College of New York in Spring 2019 by Grant Long as part of the Tech-in-Residence Corps program.

Homework for the course "CSC 59970 – Intro to Data Science" delivered at the City College of New York in Spring 2019 by Grant Long as part of the Tech-in-Residence Corps program.