Simple data analysis walk-through from one novice to another: Using Transportation Data

In this article, I attempt to demonstrate that with a minor amount of coding experience, a bit of data background, and a pinch of study and effort —even an inexperienced individual can discover insightful information from data.

Just a little background which may help provide a preface:

I’m not an analyst.

I’m not actually technically a “data” anything…

My official position is a Planner. It just so happens that what I “plan” is data and data-related activities (collection, compilation, QAQC, management, and reporting)

Sometimes, it feels like I’m swimming (or drowning) in data and yet I don’t really get the…

A novice’s journey in the Udacity Data Science Nanodegree Capstone Project

Photo by Laura Chouette on Unsplash (reframed by author)

The Starbucks Capstone Project:

This article details the capstone project from Udacity Data Science Nanodegree program which is compromised of simulated data, containing offer portfolio details, customer profile details, and transcripts of interactions from the Starbucks rewards mobile app.

The task is to combine the available datasets and determine which demographic groups respond best to which offer types.

Please see notebook in the opening section (1 Udacity’s Introduction) for more details from Udacity concerning the project.

Outline for this article:

  1. Data
  2. EDA (exploratory data analysis)
  3. Preprocessing/Engineering
  4. Modeling
  5. Issues & Conclusions

Potential Business Questions:

  1. What offers and offer types tend to perform well and why?
  2. Which, if any, demographic groups can be…

Why your State Department of Transportation should look carefully at their federally procured Travel Time Data

In another article titled “Data Analysis for the Non-Analyst”, I showed how an inexperienced individual can still discover insightful information from travel time data.

Now, I will show how an individual or entity (such as a State DOT) can use the same workflow and dataset to answer questions like:

  • How reliable is the Travel Time Data?
  • Does cleaning the data affect the measures for business units?
  • How can business units prioritize projects accordingly?


  • How to assess Reliability of your Travel Time Data
  • How to Clean and Transform your Travel Time Data
  • How to assess Measures for Business Unit Planning

Quick background:


erik mason

Data Planner and Data enthusiast at State of Alaska — Juneau, Alaska

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