Data Resources: Data Training Resources

Excel, R, Tableau, ArcGIS

R, Python, Data Management, etc

This is a collection of interactive tutorials for journalists, activists, researchers and students to learn about tools by the Sunlight Foundation and others to unlock government data.

Microsoft Excel, SPSS, and MATLAB

Stacks ; G70.212 .C37 2024

Stacks ; G70.212 .S55 2023

Stacks ; G70.217.G46 R49 2023

Learn to search, browse, download, understand, graph and mapping these data sets.

1000+.

Activities, resources, programs, workshops

The National Forum on Education Statistics has created online courses based on some of its best practice publications. These courses, which include multimedia videos, PowerPoint™ presentations, notes, quizzes, and other supporting materials, offer the education community an opportunity to learn about important education data issues. These resources can be used to train at many levels.

Offers tutorials, background materials and other resources for working with large spatio-temporal datasets like those offered by the NEON project.

-Remote Sensing Data
-Python
-time series charts, histograms, statistics

-The first sub-project (1_mental_health_project) used the U.S. Census Bureau's Household Pulse survey to explore correlates of mental health during the COVID-19 pandemic.
-The second sub-project (2_education_demographics_project) used the National Center for Education Statistics' National Household Education Survey and Common Core of Data along with the Texas Education Agency's graduation data to explore relationships among educational outcomes, student and family demographic variables, and county demographic diversity within the 12th grader population.
-The third sub-project (3_economics_employment_project) used the U.S. Census Bureau's Current Population Survey and a wide range of financial data (COVID-related spending, Medicaid spending, GDP, and minimum wage) to explore the relationship beteen government fiscal relief measures and employment during recessions.

The three sub-project folders include clean datasets, code for cleaning and analyzing the data, and interpretation of the results. These materials are suitable for a range of learners within data science, including both novices and those with advanced statistical skills.