Technology for working with data
An intuitive hands-on interface for learning how to make sense of data. Tuva supports interactive data visualization, resources for building data skills, and a library of 400+ real-world datasets, curated for use in science, math, social science classrooms from upper elementary through college.
Sources of data
My NASA Data offers petabytes of global Earth science data collected from satellites, but accessing these data in a traditional science classroom can be tricky. After nearly 15 years of offering Earth science data to educators and students, NASA continues to refine the My NASA Data program to better suit the needs of teachers and students in engaging students in authentic data analysis. (All levels)
The Climate Reanalyzer is a platform for visualizing climate and weather data using interfaces for reanalysis and historical station data, forecast maps, historical timeseries (temperature, pressure, sea surface temperature, precipitation, wind…), correlations, and more. Data are downloadable in .csv format. (High school). (The Climate Reanalyzer is created and maintained by Sean Birkel, University of Maine Climate Change Institute.)
Data Nuggets are activities that bring real scientific data into the classroom, guiding students through the entire process of science while building their quantitative abilities. (Created by Elizabeth Schultheis and Melissa Kjelvik at Michigan State University, with support from the National Science Foundation and others).
Blogs about data literacy
Beyond the Data Blog: Allen Hillery teaches Storytelling with Data at Columbia University and is passionate about communicating the impact that data visualization and literacy has on our daily lives. He’s working with Bushwick Generator, a tech incubator in Brooklyn, NY to help upskill and employ NYC’s underserved young adults via the tech ecosystem. This will be done through a data literacy coursework with real life examples that will provide students to read, understand, create and communicate data as information. In addition we will be working with tech companies to host career pathway sessions.
Our evolving thinking about how to build data literacy is grounded in a rich body of statistics, math, and science education research published over the past 25 years. Here is our ongoing list of articles, books, and web resources about data literacy, grouped by themes such as:
- How students engage with statistical thinking and reasoning about variability
- How students graph and talk about data
- How they make informal inferences at an exploratory level
- Technology and data literacy
- Learning standards and data literacy
- Teachers and data literacy… and more.
Master’s in Data Science.org offers a guide, What is a Data Scientist?, which can help individuals explore whether a data scientist career is right for them. The guide covers what the career entails, job prospects and typical salary, as well as educational requirements to enter the field.