Welcome!  I created this site as a hobby and because I couldn’t find anything similar in existence.  Analysts who work in the public sector are similar to their private sector peers in many ways, but I thought it would be interesting to focus on the nuances, challenges, and perks that we face as (insert: data, IT, systems, operations, management, you-name-it) analysts in bureaucracies.

This will be a place to get helpful information on professional development, career advice, and software reviews.  I also plan on writing thoughts on my experiences in the public sector with the hopes of sparking some conversations.  I will invite guest bloggers and ask for reader suggestions on topics.  Finally, I’ll be sharing the occasional data-related research project or share some from around the web.


Public Sector Professional Development: Data Analyst, Part 1: Math!

Professional Development is unfortunately non-existent for many of us in the public sector.  Besides the skills I gained in college and grad school, I am self-taught as far as practical data analysis.  So, let’s take PD into our own hands with online learning.

This series will focus on the steps needed to refresh our memories in math and statistics; move on to data science and analyses of various degrees of difficulty (including resources for Python and R); and finally move on to visualization and machine learning–all with practical applications to our work in public sector data analysis.

To start off the series on Professional Development, I will focus on the building blocks for data science and analysis:  math and statistics.  This might seem like an unnecessary step, but as data analysts we need to have a solid foundation in both subjects to move on to more advanced topics.

Math Must-Haves

Coursera course on basic data science math skills

Khan Academy course on linear algebra

Khan Academy course on multi-variable calculus


Coursera course on probability

Coursera course on inferential statistics

Coursera course on linear regression

Coursera course on Bayesian statistics

Intro to Data Accessibility: Resources for the Public Sector

The recent push for open data and data transparency has pushed the public sector to address issues of equity and accessibility. From the Federal level (Section 508 standards) to the local level (for example, NYC’s Open Data Law), accommodating people with visual disabilities as well as those without technical training or data analysis skills has become more of a priority.

We as public sector analysts have both a moral and legal obligation to make our public-facing data as accessible as possible.  Some of us are already aware of the standards for data accessibility since the Section 508 standards have been out for a while; others are just getting started “remediating” their reports or trying to figure out how to create new, accessible versions.  Here is a quick introduction to resources I’ve been using to navigate the often confusing standards:

  1. U.S. Department of Health and Human Services

A great primer for all Microsoft Office files, Adobe PDFs, and HTML (for those of us who post tables within web pages)

      2. For Microsoft Office and Adobe PDFs, you can also go straight to the source:

Microsoft Excel 


3. NYC Open Data Resources

This document specifically outlines what is a public data set and the general outlines of best practices

An important note for spreadsheets here: every report should have a “data dictionary” page that’s understandable to the average person.  NYC Open Data version

4. The standards that are driving most accessibility efforts are the WCAG 2.0

Most of these standards have to do with web programming, but there are some essentials in here for those that use web-based tables and visualizations.

More to come…

Government Data Site of the Week

The Civilian Compliant Review Board, an independent agency in NYC that investigates complaints of various alleged police misconduct, created the “Data Transparency Initiative.”  Using Tableau, the agency has visualized a decade of complaint and allegation data, along with aggregate analysis of (alleged) victims and police officers:
Data Transparency Initiative

I found this project really admirable for a city agency to take on.  Using a tool like Tableau is especially appreciated, and offers insights into an often inaccessible topic.

Dashboard 1