Explore my Portfolio

🏅 HNG Internship 12 – Finalist (Data Analytics Track)

One of just 42 Data Analysts selected from over 2,000+ in a competitive, real-world tech internship with 11,285 total participants. Recognized for excellence in data storytelling, dashboard design, product analysis, SQL modeling, and cross-functional collaboration with developers, marketers, designers, and QA teams on the ShopDesk SaaS project. Key strengths demonstrated: Data quality checks • Automation-ready pipelines • Customer insight analysis • Forecasting • Leadership under pressure

Invoice Data Warehouse Design for Sales Insight & Scalable Reporting

This project showcases how I transformed raw invoice data into a structured dimensional model using the star schema approach. I designed a sales-focused fact table and supporting dimension tables for customers, products, stores, and time to enable flexible analysis and business reporting. I applied Slowly Changing Dimensions (SCD) strategies to maintain historical accuracy, built a clean ETL pipeline, and ensured data quality at every stage. The final model supports drill-downs, trend analysis, and future scalability—demonstrating my ability to bridge raw data and business insight.

Advanced Geospatial Dashboard for Election Integrity & Outlier Analysis – Anambra 2023 - PYTHON & POWER BI

This project analyzes election integrity in Anambra State, Nigeria, using advanced geospatial and statistical methods. I combined clustering algorithms (like DBSCAN) and outlier detection techniques (z-score, IQR, Local Moran’s I) to identify polling units with unusual voting patterns in the 2023 elections. The analysis included spatial hot spot mapping (Getis-Ord Gi*) and machine learning checks to confirm anomalies based on population size, turnout history, and voting trends. I also compared the 2023 results with historical data and demographics like urban vs. rural spread. To present the insights, I built an interactive Power BI dashboard with drilldowns, dynamic maps, anomaly highlights, and party-level breakdowns. The project ends with a ranked list of suspicious polling units and recommendations for electoral monitoring—making it a valuable tool for real-time election assessment.

Keyword Insight Strategy with Google Tools for SME Product Adoption (ShopDesk)

I conducted a keyword-driven market analysis using Google Keyword Planner and SEO tools to understand what small and medium-sized businesses (SMEs) actively search for when exploring inventory and sales management solutions. By analyzing real search behavior, I identified high-intent terms and grouped them by buyer stage, awareness, consideration, and decision. This helped ShopDesk refine its product messaging and better position itself to early adopters. My insights supported go-to-market strategy, targeted outreach, and product relevance for key customer segments. This project deepened my passion for using data to understand customer needs and shape smarter business decisions, skills I aim to apply in customer success, product performance, or data analyst roles.

Customer Segmentation & Buyer Persona Strategy for ShopDesk

Led the development of a detailed customer segmentation and buyer persona framework for ShopDesk, a retail-focused platform serving SMEs. Conducted market research, behavioral mapping, and user profiling to identify high-value customer segments and align communication strategy. The insights from this project improved product targeting and informed early adopter outreach during go-to-market planning.

Product Performance Prediction Using Linear Regression, Average Growth, and Simple Exponential Smoothing (SES

In this project, I analyzed real-time product check-in data from the HNG Internship to predict which product teams would rank in the top 5 by the end of the week. The data was collected from a public leaderboard channel where teams posted daily check-in scores. I cleaned and structured the raw dataset, applied multiple forecasting techniques—including Linear Regression, Average Growth, and Simple Exponential Smoothing (SES), and evaluated their performance based on realism and trend behavior. Linear Regression was selected as the most reliable method for this case due to its ability to model score trends and fluctuations over time.

Product Network Analysis for E-commerce Optimization (Amazon Co-Purchase Network) with Python

I studied how products on Amazon are often bought together. Think of it like figuring out which items are “friends” because shoppers often buy them in the same order, like buying a phone and a charger. I used Excel, Python and a tool called NetworkX to build a network (like a map) showing how products are linked. I used smart techniques (called community detection algorithms) to group related products. I looked at how some products “connect” different categories, like a product that people from two different interest groups both like. My findings can help e-commerce businesses: Give better product recommendations. Create smart bundles (grouping items that go well together). Understand how customers shop. Target the right customers with smarter inventory and marketing.

Procurement Strategy & Risk Analysis | High-Value Supply Chains & Cost Optimization

I and my team worked on creating smart buying strategies for stainless steel used in special pipes (VIP) in a cold-temperature industry (cryogenics). The goal was to avoid risks, manage costs, and make sure the materials are always available. We looked at how steel prices change and what could go wrong in the supply chain (like delays or high costs). We created a strategy to buy from different places: 50% from Asia (cheaper and reliable for large orders), 30% from North America (better quality and fast delivery), 20% from Europe (innovation and advanced tech). You explored ways to reduce financial risk (like price increases or supply problems) by using forecasting, supplier diversity, and backup plans. Why it matters: The work helps companies: Save money, Avoid supply problems, Make smart, data-based buying decisions.

App Performance & Market Trend Analysis on Google Play using Power BI & Python

This project explores Google Play Store app performance using Power BI and Python, providing deep insights into app installs, user ratings, and revenue trends. By integrating external datasets and predictive analytics, the dashboard uncovers key factors influencing app success. An interactive dashboard was built to analyze app performance across different categories, price types (free vs. paid apps), and user sentiment analysis from reviews. The insights highlight top-performing apps, market share comparisons, and competitive analysis between Google and Apple Store apps. Through data visualization techniques, including trend analysis, sentiment insights, and category-wise app performance, this project delivers actionable strategies for app developers, marketers, and stakeholders aiming to optimize app revenue and engagement.

Marketing Campaign Effectiveness Analysis using Python (EDA | ROI | CTR | CPC - Optimization)

This project focuses on analysing marketing campaign data to improve customer acquisition and engagement strategies. Using Exploratory Data Analysis (EDA), I examined key metrics such as ROI, CTR, CPC, and Conversion Rate to identify high-performing campaigns and optimize marketing spend. The analysis revealed that Influencer and Search Ads drive the highest ROI, while Email campaigns underperform. I also found that higher CTR leads to better conversions, but increasing CPC does not always improve ROI. Moving forward, we plan to integrate predictive modelling and A/B testing to enhance campaign effectiveness and audience targeting.

Historical CO2 Emissions Dashboard (1750–2023) | Power BI & SQL for Climate Insights

The CO2 data used for this analysis was gotten from the official "data in our world". In this project, I analysed the global CO2 emissions data, starting with SQL for the data cleaning and preparation process. Using Power BI's Power Query, I integrated multiple datasets into a unified model and developed an interactive dashboard. The dashboard highlights emission trends, top emitters, and growth rates over time, providing actionable insights into climate change and sustainability efforts.

Advanced Excel Dashboard for Business Strategy & Performance Optimization

In today's competitive business landscape, leveraging data to inform strategy and optimize performance is essential. This project presents a comprehensive Excel analysis project that demonstrates on; data cleaning, preparation, analysis, pivot table reporting, visualization, and scenario analysis. The goal is to highlight insights crucial for decision-making and showcase advanced Excel skills applicable to roles requiring analytical and strategic expertise.

Mock Server API Testing with Postman | CRUD, Validation & Debugging Workflows

A practical project showcasing my ability to create and test APIs using Postman. This includes building a mock server to simulate shipment tracking, performing CRUD operations, validating responses, and debugging workflows. It reflects my commitment to mastering API testing, problem-solving, and continuous learning in data integration.

API Data Integration & Testing with Postman & Python | CRUD + Automation Flows

A hands-on journey into mastering API workflows using tools like Postman, JSONPlaceholder, and JSONFormatter. This project highlights my ability to create, test, and validate API requests while exploring CRUD operations, data aggregation, and error handling. It also reflects my passion for learning, problem-solving, and continuous improvement in data integration and testing.

Transport Logistics Dashboard: Shipment Analytics & Real-Time Tracking

This automated dashboard aims to enhance logistics efficiency by providing visibility into operational performance, tracking shipments, and identifying improvement opportunities through real-time analytics. It features a dynamic auto refresh which is set to occur every 8 hours ensuring real time updates on order statuses, delivery performance, and cost metrics. This dashboard also captures Shipment Analytics by tracking total distance covered in the course of deliveries, weight shipped , and the total cost of logistics operations. Visualizes order statuses (delivered, pending, and in-transit) to monitor progress and identify bottlenecks.

Automated Email Notifications for Delivery Tracking using Python & AWS SES

This project focuses on automating email service. This is a data integration project with a logistic dataset having different variables such as ShipmentID, Origin, Destination, Status, EstimatedDelivery, and CustomerEmail. Using AWS SES (Simple email services), I configured a verified email identity to send emails automatically to customers when the statuses of their orders reach the point of delivered. Python boto3 library was also used to make adequate interactions with the AWS SES for programmatically sending emails. A schedule mechanism was also implemented to check the status of shipments every 6 hours.

Starbucks Store Performance Analysis using R | EDA, Trends & Visualization

This is a detailed Exploratory Data Analysis (EDA) of Starbucks, a globally recognized coffeehouse chain with over 24,000 retail locations in 70 countries. Using data sourced from Kaggle, the analysis highlights key insights into store distribution, customer trends, and operational patterns. The project employs data cleaning, manipulation, and visualization techniques, presenting findings such as the top cities and countries hosting Starbucks stores, ownership type distributions, and the impact of time zones on customer preferences. Additionally, advanced statistical tools like correlation heatmaps and regression models are utilized to uncover deeper patterns and relationships within the dataset

Sales Data Analysis using SQL | Trends, Transactions & Customer Insights

This project focuses on performing advanced data analysis on a comprehensive sales dataset (sales_data_sample). Utilizing SQL, the project demonstrates powerful techniques to derive insights into customer spending patterns, sales trends, and overall performance metrics. The dataset contains over 700 rows of sales information, covering various customer transactions, including order details, sales amounts, and product information.

Product Circularity & Automation in Refurbishment Sector | Python & Power BI

This project focuses on analyzing the condition and circularity of refurbished devices to promote sustainable practices. By leveraging Python for data analysis and automation and Power BI for creation of interactive dashboards. The aim is to extract insights that align with circular economy principles. The dataset used in this project was sourced from eReuse.org, a platform dedicated to promoting the reuse and refurbishment of electronic devices. The dataset provides comprehensive information about various electronic devices and their conditions, making it highly relevant for analyzing circularity practices.

Market Analysis for Digital Sustainability Platform (PES) | Environmental Services

This project focused on integrating behavioural analysis, market research, and performance metrics to foster sustainable practices. Designed and implemented a digital platform, ALC EcoRepair, to connect private agencies with environmental service providers, such as farmers, for carbon footprint reduction and sustainable practices. The platform integrated environmental, social, and governance (ESG) frameworks to promote decarbonization, optimize soil remediation, and enhance agricultural practices. I was not only the part of the team to realise this final project but also the team website developer. To have a better view of our website, please view on PC https://chidinmaukandu8.wixsite.com/my-site-10

Fleet & Reservation Management System | CRM Integration & Booking Optimization

This project explores the design and implementation of a comprehensive Integrated Reservation and Fleet Management System (RFMS) to address inefficiencies in manual booking and fleet operations. The RFMS incorporates features like automated booking, real-time vehicle tracking, and customer relationship management (CRM). By leveraging digital tools and data analytics, the project aims to reduce downtime, enhance customer satisfaction, and improve operational efficiency. Additionally, the financial analysis demonstrates the system's cost-effectiveness and potential for long-term profitability.

Behavioral Economics Analysis using Ultimatum Game | Regression & Fairness Insights

This project explores the decision-making dynamics within the Ultimatum Game, a cornerstone experiment in behavioral economics. By performing regression analyses on both responder decisions and proposer offers, the study uncovers key factors influencing fairness, negotiation, and economic behavior. The findings provide valuable insights into human behavior and decision-making, offering practical applications for strategy, policy design, and economic modeling. This analysis demonstrates the integration of data-driven methods to address complex behavioral questions

Video Game Sales Trend Analysis | Genre, Market Leaders & Geo Insights (Power BI)

This analysis was focused to identify clarity on the geographical distribution of sales within companies in the video game industry, the top-performing games and platforms and finally the publishers leading the market in global sales. To determine this, I used an interactive dashboard on Power BI to visualise sales, genres and platforms, mapped out regional sales distribution and identified dominant regions using geographical charts. Included drill-through capabilities for in-depth analysis.

Amazon Sales & Product Trend Analysis | Power BI Dashboard

Using this dashboard, I was able to determine which product performed best in terms of sales and unit volume. Sales trends across cities, states, and dates, seller performance and product-specific trends. Created a multi-tab dashboard in Power BI showcasing sales trends by city, states and dates. Incorporated product-level and seller-level insights to identify top-performing products. Used visualization such as bar charts, line graphs, and filters for an interactive user experience.

Marketing Performance Dashboard | Revenue, Profit Margin & Product Trends

This project was carried out using Power BI to identify high cost products that lowered profit margins, regional profitability variations and product performance and market trends. An interactive dashboard was built to centralize data and visualize profit , revenue, cost and margins. To discover top-performing products and regions, clear identification on products with high cost but low returns. This project demonstrates the power of data visualization to uncover actionable insights for strategic growth