The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Back to feedPost
Built and maintained a Python pipeline that pulls together data from 5+ independent sources with different formats and structures, and merges them into a single, consistent analysis framework. Used to track how a complex system changes over time, surfacing trends that aren't visible when each data source is looked at separately. Ongoing project, 12+ months.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Related posts
Messy Data In. Clearer Decisions Out. 📊
Over the past year, I've noticed most businesses don't struggle because they lack data — they struggle because that data never turns into a decision.
That's the gap I work in:
→ Cleaning and analyze data with Python & R
→ Turning findings into investor-ready pitch decks
→ Polishing the final write-up so nothing gets lost in translation
If your team has data sitting in spreadsheets with no story attached, or a pitch deck that needs to actually land — that's exactly where I can help.
Open to projects. Let's talk 👇
Behind every clean chart is a messy spreadsheet 🧹
This is what a real data cleaning job looks like before it becomes an elegant bar chart — duplicate rows, missing values, inconsistent formatting, all sorted out with Python and Pandas.
#collage attempt: a look inside the data analyst's actual desk, not just the pretty output.
3 mistakes that will eventually make this board useless. 📉
I've reviewed several client dashboards over the years, and the same 3 problems show up over and over:
1️⃣ Too many charts, no clear takeaway — if I can't tell what to do after looking at it, it's decoration, not a dashboard.
2️⃣ Colors with no consistent meaning — red means "unacceptable" on one chart and "category 3" on the next. Pick a system and stick with it.
3️⃣ No comparison point — a number alone means nothing. "Revenue: $12,480" tells you nothing. "Revenue: $12,480 (+18% vs last month)" tells you everything.
Fix these three, and your data stops being numbers on a page — it starts being something people actually use to decide.
What's the most common dashboard mistake you've seen? 👇
