How I Conquered the 30 Day Chart Challenge?

A Self-Reflection and Lessons Learnt

I recently completed the 30 Day Chart Challenge (See all my contributions for 2025 here), a fun project vital for developing my portfolio and technical skills. While I finished it a few months after the initial community run, it is an experience rich with lessons I believe are important to reflect on.

For those who don’t know what it is: It is a community-run challenge in which every day you are given a topic to prepare a chart. Participants are encouraged to use any tool, program, or language, making it accessible to everyone, whether they choose to use MS Paint, Excel, Power BI, Python, or R.

Deciding on a Strategic Theme

While the Challenge provides a topic for each day, deciding on a theme to follow throughout the 30 days is a crucial strategic decision, as pointed out by fellow data analyst Gregers Kjerulf Dubrow.

As an astronomy hobbyist, my strategy was to choose a running theme: astronomy, which allowed for deeper exploration and skill demonstration across the 30 days. Learning more about the orbital trajectories of the planets or how exoplanets are discovered made the challenge both fun and easier to find relevant datasets. NASA and its various labs, the ESA, and JAXA all publish publicly and freely available data.

However, there were many days in which the chosen theme did not provide any benefits: Fossils and Astronomy are hard to combine. I let my creativity take over, instead of being limited by my own constraints.

Process is Simple; Data Sourcing is the Core Skill

Preparing a chart for each day is actually quite straightforward: check the daily topic, find the data, and choose the right visual.

As anticipated, data sourcing proved to be the most time-intensive phase, consuming nearly 75% of the total effort, even with a specific theme. After spending roughly 10% of the time on cleaning and preparation, the remaining part is for preparing the visual itself. This experience honed my ability to efficiently scout and vet publicly available data.

Suggestions for anyone who plans to join the challenge:

• Generative AI tools are actually quite good at pointing where to find the data sets: Gemini, Chat GPT and Claude all provide references if you specifically ask for it.
• Mock-up data sets from Kaggle can help accelerate the process when time is short.
• Searching for #30DayChartChallenge on social media gives enough inspiration to narrow down your data hunt.
• You can always go back to a previous day’s chart to make changes.

The Art and Science of Visual Selection

Do you prepare a line chart? Bar chart? Or a map? Visual selection is crucial for professional data communication, as there are certainly wrong ways to visualise data. To ensure visual integrity and clarity, I followed a structured approach. I relied on traditional pen-and-paper sketching to conceptualise the narratives, supplemented by excellent online resources such as:

• Yan Holtz’s excellent website (https://www.data-to-viz.com/)
• Ferdio’s DataViz Project (https://datavizproject.com/)
• Asking Generative AI for suggestions.

Consistency is Key but Avoid Burnout

I maintained a strict discipline, working on the visualisations daily to ensure continuous progress. While consistency is key, this should be entertaining as much as challenging. If you feel like you have no time, and the challenge is getting the best of you, there is no point in forcing it. On the days where I was busy or just did not feel like it, I used mock-up data, prepared more straightforward charts, or just moved on to come back to that topic another day.

Challenging Technical Proficiency

To elevate my technical proficiency, I focused on using Python (specifically Matplotlib and Seaborn) for the core visualisations, only using Illustrator for final polish. While I have used Python for data analysis in the past, this challenge was a deliberate effort to gain extensive, hands-on experience in programmatic visualisation, which proved to be excellent. Python is a very flexible language and there are so many resources out there for those of you who want to learn or improve your knowledge.

Next year my goal is to use a different data visualisation for each day to expand my repertoire: no more multiple line charts, choropleth maps, or horizontal bar charts!

Overall: A Rewarding Professional Development

Seeing how creative people were in the challenge made it more enjoyable for me. I constantly checked the #30DayChartChallenge hashtag on social media to see what amazing contributions people have made. There are many who took the topic of the day very literally (including me), but there are also so many people who interpret the topics in more creative ways.

Furthermore, using personally important data sets made the challenge more entertaining for me. As a Turkish citizen who lives in Fiji, I used data sets related to Turkey and Fiji quite often. Even when I compared multiple countries, I wanted to specifically check how Turkey and Fiji are doing.

Calculating the orbital trajectories of the planets using Kepler’s Laws was a particularly rewarding technical exercise, and I encourage any aspiring data analyst to take on the 30 Day Chart Challenge.