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Resources for Planning Data Literacy Training

US Federal CIO Council – Data Skills Catalog, Playbook & Case Studies
DataSF Data Academy
Australia Public Service Data Skills and Capability Framework
NGO Data Literacy and Assessment Tool
Training Resources
Other Resources of Note
Lessons for Program Design and Implementation

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US Federal CIO Council - Curated Data Skills Catalog

Primary target users are CDOs of Federal Agencies
Organized around the Federal Data Lifecycle Roles + Skills Needed for Each Phase:

Define, Coordinate, Collect, Access, Analyze, Visualize, Disseminate, Implement, Assess

Identifies available learning opportunities for each Role

Training open to all: Census Academy, USGS Data Management Training,
Training limited to selected Federal employees – see curriculum for the CIO (online) Data Science Training Program

https://resources.data.gov/assets/documents/fds-data-skills-catalog.pdf

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Disseminate Data: Provide Multiple Avenues for Release of Data

Practices

Convey Insights from Data
Use Data to Increase Accountability
Convey Data Authenticity
Communicate Planned and Potential Uses of Data
Explicitly Communicate Allowable Use
Promote Wide Access
Diversify Data Access Methods
Review Data Releases for Disclosure Risk

Skills

Accessibility standards (508 compliance)
Communication
Companion materials creation
Data formats and API technology
Intellectual property rights
Negotiation
Relationship building
Understand stakeholder needs and requirements

https://resources.data.gov/assets/documents/fds-data-skills-catalog.pdf

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Improving Agency Data Skills Playbook

Step 1: Identify any Critical Data Skills Needed for the Agency
Step 2: Assess Current Staff Capacity for Needed Data Skills
Step 3: Perform a Data Skills Gap Analysis
Step 4: Identify and Execute Ways to Meet those Needs

Also includes Metrics + Related Data Actions

https://resources.data.gov/assets/documents/assessing-data-skills-playbook.pdf

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Federal CDO Council Data Skills Training Program Case Studies

FDIC Data Literacy Pilot

Open to all employees, organized around 5 personas representing roles
Mix of e-learning, instructor led and brown bag sessions

HHS Data Science CoLab

train cohorts of 30-60 employees and contractors
use agency-specific datasets + capstone project

Census Bureau Data Science Training Pilot

Use similar cohort approach
Guided by data science advisory council
Includes mentorship component

https://resources.data.gov/assets/documents/CDOC_Case_Studies_Final_v3.pdf

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DataSF – Data Academy

Courses offered to employees of City & County of SF
Courses taught by City employees – in person + some online
Curriculum Offerings Organized By:

Analysis
Data Management
Data Visualization
Excel
Information Design
Tableau
PowerBI
Process Improvement

https://datasf.org/academy/

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Australia Public Service Data Skills and Capability Framework

Identified specific data roles: data analysts, data scientists, data policy and law experts, data infrastructure engineers, data architects
Framework with 4 components:

General data literacy training for all employees
University courses – tailored training for specialized roles
Data fellowships – intensive 3-month internship for selected data experts

Most with Data61 – National Science Agency’s data sciences arm – see https://data61.csiro.au/en/About

Data training partnerships

https://www.pmc.gov.au/sites/default/files/publications/data-skills-capability.pdf

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Australia Public Service Data Skills and Capability Framework

General data literacy training - five areas

Providing evidence for decision makers
Undertaking research
Using statistics
Visualising the information
Telling the story.

Resources

APS Data Literacy Learning Guide
Workshops
E-Learning Courses

https://www.pmc.gov.au/sites/default/files/publications/data-skills-capability.pdf

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NGO Data Literacy Assessment Tool

Simple, designed for Non-Governmental Organizations (NGOs) and academic institutions
The four levels are defined for each competency
A spreadsheet tool supports assessment of individual data literacy maturity

https://datenschule.de/files/workshops/Kurzversion-Helena-Sternkopf.pdf

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DOT Data Literacy Activities

Oregon DOT – Developing Training Modules
Caltrans – Training Program Design
Nevada DOT – Data & Analytics Program including literacy
Minnesota DOT – 4 modules – geared to data stewards

https://dot.ca.gov/-/media/dot-media/programs/research-innovation-system-information/documents/preliminary-investigations/pi-0285-a11y.pdf

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Data Literacy Training Resources

EdX	Offers a wide variety of courses in basic data literacy, analysis, data science and application of various tools (R, Python, Excel, Tableau, etc.). Most courses can be audited for free.
Coursera	Five course data literacy series, emphasizing data interpretation and statistical analysis. Free to audit.
Udacity	Courses for different data roles (data engineer, data scientist, data analyst, business analytics, data product manager, data architect, marketing analyst) + skill-specific courses like data visualization and data structures and algorithms. Many are free.
LinkedIn Learning	Offers many data-related courses, including a “Build Essential Data Skills” learning path introducing exploring and describing data, basics of data science and statistics, data privacy, data governance, and data-driven decision making. Offers one free month.
Data Camp	Five course data literacy series providing introductions to data science, machine learning, data visualization, data engineering and cloud computing. Three of the five courses are free.
The Data Literacy Project/Qlik	Mix of free and for-fee courses in data fundamentals, data-informed decision making, analytical techniques, advanced analytics. Offers data literacy and data analytics certifications.

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Data Literacy Training Resources

eLearningCurve	Comprehensive for-fee curriculum covering information management, data quality, data governance, data stewardship, BI & analytics, data science and master data management. Offers data literacy certification based on their Data Literacy Body of Knowledge (DLBOK).
Data Literacy, LLC	Three online course series covering data literacy fundamentals, level 1 (data interpretation) and level 2 (data analysis). Also offers instructor-led training and a data maturity assessment for teams.
CIO.gov	Data science training program – available for free to federal employees. Covers data science foundations, data fluency, data visualization, AI/Machine Learning, design thinking, analytics, python, PowerBI, ArcGIS, Tableau, databases/SQL
Open Data Institute	UK-based membership organization – has comprehensive data literacy framework with online and instructor led courses. Offers 2 free e-learning courses (open data essentials; finding stories in data)
National Highway Institute	Safety Data and Analysis Fundamentals Training Role of Data in Transportation Performance Management Data Archiving and Analytics for Planning, Operations and Safety HPMS-focused courses

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Other Resources of Note

Gartner – data literacy toolkit based on personas, includes assessment tool (for Gartner customers)
Data to the People – Databilities literacy framework and assessment tool https://docs.wixstatic.com/ugd/1ff4ae_14805e0c8ef14b54bdafd38e44d5de23.pdf
Data Literacy Body of Knowledge – eLearningCurve https://ecm.elearningcurve.com/Data_Literacy_Body_of_Knowledge_s/222.htm
Data Science Skills Competency Model – IBM https://www.ibm.com/downloads/cas/7109RLQM
Statistics Canada – Review of Data Literacy Frameworks https://www150.statcan.gc.ca/n1/pub/11-633-x/11-633-x2019003-eng.htm

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Lessons Learned – Training Program Design

Target program to your needs – be clear on your goals
Define roles & tailor training to each role
Consider both data/information producer and consumer/user
Consider technical, managerial, and “soft” skills (esp. communication, project management, analytical thinking and ethics)
Competency definitions should consider motivation and attitude – not just KSAs
Think about how training outcomes will be assessed

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Training is Not Enough – Be Holistic

Leadership that values and rewards data skill development
Position descriptions with defined data responsibilities
Clear career paths and advancement opportunities
Recruiting and retention strategies for data professionals
Opportunities for the application of skills
Creation of peer networks for knowledge sharing

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