Passionate Data Analyst skilled in transforming raw data into clear, actionable business insights.
Currently leveling up my toolkit by learning Python for advanced data analysis and automation.
| Tool / Skill | Proficiency | What I Do With It |
| Excel | Advanced | PivotTables, VLOOKUP/XLOOKUP, Power Pivot, dynamic dashboards, data modeling |
| Power BI | Advanced | DAX calculations, data modeling, interactive reports & dashboards, publishing & sharing |
| SQL | Advanced | Complex joins, CTEs, window functions, query optimization, data extraction & aggregation |
| Power Query (M) | Advanced | Data cleaning, transformation, merging sources, custom functions, ETL pipelines |
| Python (learning) | Intermediate (growing fast!) | Pandas, NumPy, Matplotlib/Seaborn, data wrangling, automation, transitioning from Excel/Power Query |
- 📊 Building end-to-end business intelligence solutions — from raw data cleaning in Power Query → modeling & DAX in Power BI → interactive stakeholder dashboards
- 🗄️ Writing clean, performant SQL queries for large datasets — focusing on performance tuning and reusable patterns
- 🔄 Automating repetitive Excel workflows using Power Query and slowly migrating them to Python + Pandas
- 📈 Creating real-world data analysis projects — sales performance, customer segmentation, inventory trends, financial reporting
- 🐍 Daily Python practice for data analysis — mastering Pandas for data manipulation, visualization libraries, and basic scripting to complement my Microsoft stack
Current Learning Focus — bridging the gap between traditional BI tools and programmable data analysis with Python.
Currently comfortable with HTML, CSS, and vanilla JavaScript — creating simple, modern-looking web pages.
This dashboard provides a snapshot of sales performance, likely over a 5-month period (January to May, based on the monthly chart). It covers revenue, profit, costs, and breakdowns by sales reps, cities, products, and months. Note: There's a minor inconsistency in the aggregated figures (e.g., revenue ₦2.3Bn minus COGS ₦2Bn suggests ₦300M gross profit, but reported profit is ₦466M—possibly due to additional income, mislabeled costs, or dashboard rounding). I'll base the analysis on the visible data, focusing on trends, top performers, and opportunities. All figures are in Nigerian Naira (₦).
The dashboard highlights a massive total market capitalization of $12.61 trillion for the top 50 US tech companies (as captured in that period). This figure aligns well with historical data around late 2023/early 2024, when the "Magnificent Seven" (Apple, Microsoft, Alphabet/Google, Amazon, Nvidia, Meta, Tesla) and other big tech players collectively approached or exceeded ~$12–15 trillion during bull runs driven by AI hype, cloud growth, and post-pandemic recovery. (Note: By early 2026, the broader US tech sector's total market cap has grown significantly larger, often exceeding $40 trillion across 1,000+ companies, showing continued explosive growth.)
Amazon stands out as the clear revenue king with $513.98 billion in annual revenue — far ahead of the pack, thanks to its e-commerce + AWS cloud dominance. Apple follows strongly at $387.53 billion, powered by hardware (iPhone ecosystem) and services. The rest of the top tier includes: Alphabet (Google): $282.83 billion (advertising + cloud). Microsoft: $204.09 billion (cloud, software, enterprise). Meta: $116.6 billion (social/advertising).
The bar chart emphasizes how a handful of giants generate the lion's share of revenue, with a steep drop-off after the top 5–10.
"The goal is to turn data into information, and information into insight." – Carly Fiorina ✨
Open to data analysis collaborations, feedback on projects, or just geeking out over dashboards & queries!