Skip to content
View SeanW-Data's full-sized avatar

Block or report SeanW-Data

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Content in all repositories owned by your account will be closed.
Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
SeanW-Data/README.md

Hi, I'm Sean πŸ‘‹

Aspiring Data Analyst | Excel β€’ SQL β€’ Power BI β€’ Python


πŸ‘€ About Me

Before I worked with data professionally, I ran a business for seven years.

I tracked revenue, monitored margins, built reporting in Excel, and made pricing decisions based on data.
The analytical thinking was already there β€” I just didn’t have the title.

So I built the experience from scratch.


πŸš€ What I Do

I design end-to-end analytics projects that replicate real business problems β€” from raw data through to actionable insights.

  • 🐍 Data generation & cleaning (Python, pandas)
  • πŸ—„οΈ Data analysis (SQL, PostgreSQL)
  • πŸ“Š Data modelling (Power BI, star schema)
  • πŸ“ˆ Dashboarding with clear business recommendations

πŸ“Š Featured Projects

πŸ”Ή SLA Performance Tracker (Latest)

Python Β· PostgreSQL Β· Power BI

End-to-end service desk analytics pipeline analysing 8,200 tickets across a full year.

  • Identified SLA performance gap (70.7% vs 85% target)
  • Found key drivers: workload imbalance, reassignments, out-of-hours gaps
  • Delivered a two-page MI dashboard with actionable recommendations

πŸ‘‰ Focus: Operational analytics, root cause analysis, decision-ready reporting

πŸ”— View Project


πŸ”Ή Tech Pricing Analysis

SQL

  • Identified outliers distorting the average unit price
  • Reframed pricing strategy after correcting the data
  • Quantified impact of incorrect assumptions

πŸ‘‰ Focus: Analytical thinking, data validation

πŸ”— View Project


πŸ”Ή Retail Performance Report

Power BI

  • 180% YoY growth masking underlying risks
  • Revenue concentrated in two cities
  • Delivery issues linked to specific couriers

πŸ‘‰ Focus: Business insight, storytelling, recommendations

πŸ”— View Project


πŸ”Ή Customer & Sales Dashboards

Excel + Python

  • Analysed 541,000 transactions
  • 64% repeat customers β†’ 92% of revenue
  • 14.8% cancellation rate

πŸ‘‰ Focus: Data cleaning, large datasets, KPI analysis

πŸ”— View Project


πŸ› οΈ Tools & Technologies


🧠 Approach

I document everything β€” data definitions, transformations, and logic β€” because a report that can’t be handed over isn’t finished.

I focus on:

  • Breaking assumptions in the data
  • Finding root causes, not just trends
  • Delivering insights that lead to action

🎯 What I’m Looking For

Junior Data Analyst / MI Reporting role (East Midlands or remote)

  • Contribute to real business decisions
  • Work across the full analytics workflow
  • Continue developing in a data-driven environment

πŸ“« Connect With Me


⭐ If you find my projects interesting, feel free to explore my repositories.

Pinned Loading

  1. sla-performance-tracker sla-performance-tracker Public

    Why are SLAs being missed β€” and what should be fixed first? This project answers that question using a full analytics pipeline and operational dashboard.

    Jupyter Notebook 1

  2. ecommerce-executive-dashboards ecommerce-executive-dashboards Public

    End-to-end e-commerce analytics: Python data cleaning β†’ Excel executive dashboards with slicers, CUBEVALUE, and dark-themed visualisations.

    Jupyter Notebook 1

  3. interiva-furniture-powerbi-analytics interiva-furniture-powerbi-analytics Public

    End-to-end Power BI analytics project completed as part of a college assessment, analysing a fictional UK furniture retailer.

    1

  4. sql-exploratory-data-analysis sql-exploratory-data-analysis Public

    Exploratory data analysis of a fictional tech business using PostgreSQL, focusing on revenue performance, customer segmentation, seasonality, churn, and pricing anomalies.

    1