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Data Analytics portfolio showcasing hands-on projects using Python, SQL, Power BI, and Machine Learning. I work with real-world datasets to perform data cleaning, EDA, SQL analysis, customer segmentation, KPI analysis, interactive dashboard development, and predictive forecasting. Projects include e-commerce sales analytics, Power BI business dashboards, and customer churn prediction, with a focus on converting data into actionable business insights.
The network for creativity
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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.
I designed and developed a Spotify-inspired Music Analytics Dashboard in Microsoft Power BI, combining data modelling, DAX, interactive navigation, and custom dashboard design into a single analytical experience.
The dashboard allows users to explore music data from different perspectives — from overall performance and popularity to individual artists, songs, albums, and trends over time.
What I built
- Overview Dashboard — high-level KPIs and music performance trends
- Artist Analytics — artist rankings, song counts, popularity and catalogue analysis
- Song Analytics — song popularity, duration, year-wise analysis and album insights
- Interactive Navigation — seamless movement between dashboard sections
- Dynamic Visual Analysis — monthly trends, popularity patterns and category comparisons
- Detailed Data Tables — deeper exploration beyond summary-level KPIs
- Spotify-inspired UI — dark, modern interface designed around the visual identity of a music streaming platform
Key metrics explored
- 342 Artists
- 789 Distinct Songs
- 562 Albums
- Average Song Duration: 3.28 minutes
- Average Popularity: 90
Tools & Skills
Power BI · DAX · Data Modeling · Data Visualization · Interactive Dashboard Design · KPI Development · Business Intelligence
The goal wasn't simply to create another Power BI report — it was to make the data explorable, intuitive and visually engaging.
Interested in turning your own data into an interactive Power BI dashboard? Let's connect.
