A flowchart is a picture of the separate steps of a process in sequential order. It it super useful to organize and interpret business processes, IT systems, or computer algorithms.
I draw flowcharts and process diagrams all the time in my daily work as a data scientist!
Drawing out the business process is often a first step in any project, in order to really understand the underlying business workflow and problems. I feel doing so greatly facilitates opportunity finding.
Moreover, when designing a machine learning or data science architecture — with data coming from different sources, being manipulated using different workflows, and ending up in models feeding multiple business processes — drawing the whole she-bang out really helps me personally to keep overview.
There are licensed software programs such as Microsoft Visio that allow you to create flowcharts. But there are also numerous free applications that can help you draw up a flow chart.
I’ve recently transitioned to VS Code for all my Python programming, so I really welcome this cool feature. It integrates all the flow chart functionality of draw.io right there in your IDE. Incredible!
Here’s a demo:
Here’s another demo, but with a light theme, showing how easy it is to export your diagrams to a shareable png file.
Moreover, due to VS Code’s amazing “LiveShare” feature, you can even collaborate with colleagues and build a flow chart together, simulatenously:
Many (aspiring) programming professionals competed in this challenge, sharing their learning journeys in domains from web development, machine learning, or data visualization.
With this blog, I wanted to share two of those learning journeys that stood out for me.
First, there’s Avik Jain’s 100 days of Machine Learning code repository on Github. Avik’s repository contains all learning activities he followed during the 53 days of programming he completed. Some of Avik’s entries really stood out, and I particularly liked his educational infographics:
Just look at the wonderful design and visual aids on this decision tree for dummies infographic, pseudocode and all:
Although Avik didn’t seem to have completed the full 100 days, many others did.
I have blogged about Hannah Yan Han‘s 100 days of code project before, but she definately deserves another mention here. Her 100 days revolved around data science, data visualization, and storytelling using both R and Python. You can find her #100DaysOfCode Medium page here, and her associated Github repository here.
For example, one day Hannah explored where instant noodles come from, how they are served, and whether people like them or not.
What I found so great about Hannah’s project is that she picked a novel dataset every couple of days. Moreover, she used a extremely large variety of different visualization formats. All visuals were equally beautiful, but Hannah made sure to pick the right one for the purpose she was trying to serve. If you are interested in data visualization, you seriously should check out Hannah’s 100DaysOfCode Medium page.