Getting you a better look (at data and programming)

Summary: I am building a data app with Python, and will be sharing the process and code through a weekly (paid) publication.

This is for those interested in an inside look at how a data analysis or a Python programming project is done. Think of it as virtual, at-work shadowing through the whole project If this might be you, please read on.

For those interested in the outputs rather than the process, they will still be shared through free articles.

Hi Everybody

I've been busy over the last few months helping to put together (written material, videos, the whole shebang) a data analytics course. I've done a fair bit of research for this, as well as having received feedback for much of my content created in this area over the last couple of years.

In the process, I noticed that a lot of people want to know how to get better at using data to get actionable information. It might be for a personal project, to meet new work responsibilities, or maybe they're looking to change careers.

A common advice that I see given to those folks is to "just pick a (data/programming) project and do it", and add it to your portfolio. This is good advice, but also a very difficult one to follow.

How do you learn through a skill-based project... you don't already have that skill? Quite often, this is a big ask.

This publication aims to help with this, and to complement the more formal training courses.

I think it's best described as a digital ride-along, or shadowing, for a Python data project. My goal is to provide a transparent, fly-on-the-wall view throughout the whole problem-solving process that is data analysis and programming.

In any project I've done, there's been loads of challenges which I'm sure you can relate to. They include:

  • Making good decisions on how to translate overall goals to data analysis metrics,
  • Not knowing how to implement an idea to code,
  • Learning about particular APIs, libraries, package management tools, deployment options, cloud services, and of course
  • Something not working as expected.

The idea is to share the progress, research done, mistakes made, and thought processes for the project at various states along the way.

If I spend a day resolving a dependency hell problem you'll probably read about it. It won't be just a one-liner saying "don't use library_a release xx with library_b release yy", but how I tackled the problem - from stackoverflow and github searches to resolution.

Too often, programming and data education material is structured like this:

Source: Van Oktop

Which, really is a not a map or a guide to teach you how to draw so much as just a picture of the destination. Instead, I hope to have you along for the whole process.

To start with, I'm building a basketball data analytics project, which will automatically flag standout events (player/team performances over single games or extended runs).  

The first issue is up and available for free.

Please have a read, and if you think this publication will help you learn faster than otherwise - subscribe to the publication.

For now, it's priced at basically a cup of coffee ($5) a month, and we're offering 20% off for 6 months for those who sign up in the next couple of weeks.

Visual/Noise - The Extra Pass
Learn data vis / analytics through practical, fun real-life examples. NBA and sports data visualisations, data dashboards with Python.

It is set at a very modest price for what will be at least four issues a month with tutorials / insight into a real coding project.

Feel free to reach out on Twitter also if you have thoughts.

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