tomsb459 / ada-2017-justice

Applied Data Analytics training program focused on criminal justice

Geek Repo:Geek Repo

Github PK Tool:Github PK Tool

Applied Data Analytics training program - Justice 2017

The Coleridge Initiative's Applied Data Analytics training program focused on criminal justice.

Overview

The training program is designed to address public sector challenges, but is applicable to a broad range of fields such as sociology, public health, computer science, survey statistics, and federal departments. Completion of this program will equip participants with the tools necessary to effectively manage and undergo technology and analytics projects, as well as open the door to the fields of statistical analysis, software development, and data science. This program is designed for professionals with a masters’ degree or above with some emphasis in a quantitative field (e.g. statistics, economics or computer science) or at least two years working in a hands-on, data-oriented field.

Data Available for Class

For both class notebooks and team projects, participants of the Applied Data Analytics Training Program have access to several datasets, including confidential micro-data hosted on the secure Administrative Data Research Facility platform. The datasets available include:

  • Illinois Department of Corrections (IDOC) data
  • Illinois Department of Employment Services (IDES) data
  • Census LEHD Origin-Destination Employment Statistics (LODES)
  • HUD program data

Program Schedule

Day 1: Welcome & Introduction

Day 2: Intro to Datasets & Programming

Day 3: Webscraping & APIs

Day 4: Databases

Day 5: Record Linkage

Day 6: Programming with Big Data

Day 7: Machine Learning

Day 8: Text Analysis and Spatial Analysis

Day 9: Networks

Day 10: Inference

Day 11: Visualization

Day 12: Privacy, Confidentiality, and Ethics

Final Presentations

About

Applied Data Analytics training program focused on criminal justice

License:Creative Commons Zero v1.0 Universal


Languages

Language:Jupyter Notebook 96.2%Language:HTML 3.8%