Statipy Data Explorer

Tuesday. June 25, 2019 - 3 mins

What’s data got to do with it?

Switching up an approach to creating tools, I found data journalism or data digging a fun way to prove how small decisions affect the overall outcome.

After setting your goal, you then need to determine how to measure this result. In this example, the primary metric is the number of active accounts, which is simple to measure. However, as you continue to build your product, you’ll want to consider additional metrics, such as the on-boarding drop-off rate, account closure rate and user account activity. Build these metrics into the product from the beginning so that you can verify that the product is meeting the market’s needs.

Now that you’ve identified metrics to define your goal, you can start building your product and defining the backlog. Prioritize the effort to create your metrics in the initial release so that you can continually evaluate whether you’ve reached your goal. Once you’ve measured results, you can start building functionality in a hypothesis-driven way to experiment and iterate on ideas. After each feature release, evaluate the status of your results and decide whether to iterate on the product more or move on to another goal.

-> from this Build the Right Product with Results Driven Development

Another interesting view, a common cultural connotation in work places, Gung-Hoism, Competition, Merit-based rewards and Work-volume

A point of confusion When most people think of being results-driven, they are really thinking of being data-driven. The concepts are similar, but there is a subtle, yet important difference between the two. People who are data-driven track loads of data. They collect everything in the hopes that analyzing the data will uncover some unknown truths about their products or customers. People who are results-driven, track very little data. They only collect the data that most specifically tells them if their customers are doing more of the things they want them to do.

-> Results Driven Culture

Probalistic Programming and Bayesian Methods for Hackers

Bayesian Methods for Hackers

BMH on Github

*“Satirical but True”*

What is Statipy?

Check out my previous post on the build process of Statipy - using Spotipy python wrapper.

Data Exploration

Using the superbly documented and structured Spotify Api I am able to make this diving work fruitful.

If you want to try it on your own playlist, checking out the spotify api documentation on how the JSON data is structured definitely helped me in my approach..

Due to the nature of tracks on the shared playlists, I wanted to first explore the song population over time.

I. Dates in Years Artists in Playlists grouped in to repeated track vs. non repeated track count - over all popularity in album years.