The TDC Applied Data Analytics fall 2019 training course, developed by the NYU Coleridge Initiative, will provide state-of-the-art perspectives and instruction on how to manage and analyze microdata to inform policy analysis and program operations. Scholarships and travel support are available for TANF agency staff. The goals of the course are to:
- Train TANF agency staff and their partners in rigorous and modern computational data analysis methods and tools for decision-making (topics include record linkage, machine learning, Jupyter notebooks, etc - see Syllabus for more details);
- Support less technical subject matter experts and leadership be informed consumers of these methods so that they can contribute to their effective application, in addition to learning the technical how and why of these techniques;
- Equip a wide range of agency staff (including those in leadership roles) with the skills they need to be part of truly integrated data science teams within agencies; and
- Establish new networks across agencies and geographies to address shared problems
This hands-on training program will use a decade of TANF recipient data and wage records. Participants will be grouped in teams of 5 or fewer people to answer questions like:
- What characteristics increase an individual's risk of returning to TANF?
- What are the employment outcomes of TANF leavers?
- What are the characteristics of those at-risk of not finding stable employment?
Data novices to computer experts will gain innovative new skills which can be applied directly to real-world data. The curriculum draws on best practices from both industry and government, including adherence to strict federal requirements on security and confidentiality.
Since February 2017, over 300 agency employees representing over 100 agencies have participated in Applied Data Analytics training programs.
We invite you to join the growing network of program alumni!
The TDC Applied Data Analytics course consists of two in-person modules with online/remote sessions.
In-person sessions will take place at the University of Maryland, College Park (see Program Overview and Timeline) on September 18th - 20th, and October 16 - 18th,
Who Should Attend
TANF agency staff are the target audiences of this course. However, staff from other agencies, organizations or institutions (e.g., other government agencies, academic, private, or other) are also eligible to attend. TANF agency staff have priority registration through August 10th.
Scholarships and Travel support for TANF agency staff
Scholarships for tuition and coverage of travel costs are available to participants currently employed at a TANF agency.
TANF agency employees will automatically be considered for a full scholarship (up to three per agency) upon submission of application.
Program overview and timeline
|Online, example: here (incl. security requirements)||Register ASAP, complete by September 3||About 2 hours total|
|Intro to SQL & Python
|Online videos with web-based content; weekly online 1 hour group discussions||First online discussion week of August 19 (but can be started at any time)||Up to 10 hours, suggest at least 1 hour per week|
|Introduce the program, data, and projects; includes data exploration, visualization, record linkage, and introduction to Machine Learning||September 18 - 20||3 days at the University of Maryland|
|Self-paced project work with teams; instructors available for assistance||September 21 - October 15||Up to 10 hours, suggest at least 2 hours per week|
|Focus on projects with sessions on Inference and Confidentiality||October 16 - 18||3 days at the University of Maryland|
|Complete team projects; recommend weekly check-in with an instructor||October 19 - November 5||Up to 10 hours, suggest at least 2 hours per week|
|Present final projects via Webex||November 6||30 minutes per team; up to 3 hours|
Project structure overview
Define overall policy goal: for example, improve employment outcomes of TANF recipients
Possible data project focus:
- Employment outcomes of TANF recipients
- Get, explore and link data
- Evaluate analysis on historical data, e.g. predict at-risk population to target resources
- Consider inference, confidentiality and ethical issues
- Communicating and reporting results
Activities before the in-person training program
- Security Training module
- Three 10-minute videos with a short quiz at the end of each video
- Introduction to SQL and Python
- Four-week module of online videos and programming tutorials
- Once per week discussion session hosted online (via Zoom)
Online lecture series
The program will feature recorded lectures that can be watched online
- Data Visualization
- Record Linkage
- Privacy and Confidentiality
- Day 1 - Intro & Overview
- Day 2 - Exploration & Visualization
- Day 3 - Analysis & Intro to Machine Learning
Goal between modules: run v1 of project analysis
In-person week 2
- Day 4 - Project work
- Day 5 - Inference
- Day 6 - Confidentiality
- Suggest weekly 1-hour meetings for each team
- Estimate 10-20 hours per person
- Date: November 6
- 20 minutes for each team to present, 10 minutes for feedback and questions
- Via WebEx Event