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…
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.
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:
Data Planner and Data enthusiast at State of Alaska — Juneau, Alaska