Ganiu Aina

Data Analyst •

PowerBI

Microsoft Excel

Data Analysis

Python

Data Cleaning

Plotly

Analytics

Business Administration

Business Analytics

pandas

Analytical Thinking

Available for hire fromNegotiable

Contracts

Business Intelligence and Data Analyst | Helping Business Leaders and Data Teams Unlock Insights with Automated Data Transformation, Visualization using Power Query, Power BI, Sisense, Python, and SQL | PL-300 Certified

As a Data Analyst with a track record of transforming complex data into meaningful insights, Im passionate about driving impactful, data-driven decisions. Over the past three years, Ive worked with various tools, including Power BI, SQL, Python, and Excel, to help businesses streamline processes, automate workflows, and unlock insights that improve operational efficiency and strategy alignment. In my role at DATORE, I boosted data accuracy by 35% for a law firm through automated data extraction and validation, significantly reducing manual data entry errors. For a UK retailer, I enhanced IoT data monitoring across 400+ stores, increasing device uptime by 20% and saving 100+ hours in manual checks each month. I also optimized ETL processes, improving data refresh reliability by 40% and enabling early detection of data issues. With certifications in Power BI, Power Platform, and AWS, Im continuously learning and expanding my toolkit to ensure I can deliver the most effective data solutions. I thrive on projects that require a strategic approach to data analysis and visualization, and I believe that the best insights come from a blend of technical expertise and a deep understanding of business needs. If youre looking to leverage data for real business impact, lets connect! Im always interested in collaborating on projects that challenge me to think critically and deliver results.

Projects

ADVENTUREWORKS-ANALYSIS WITH MICROSOFT FABRIC AND SQL SERVER

Objective: To create an interactive dashboard integrating a SQL view from SQL Server On-Premises into Microsoft Fabric for enhanced data analysis and insights for AdventureWorks. Tools Used: SQL Server (On-Premises) Microsoft Fabric Power BI Desktop DAX Dataflow Gen2 Approach: A database backup (.bak) from Microsoft Learn was imported into SQL Server. An On-Premises Data Gateway was set up to enable connectivity between SQL Server and Microsoft Fabric. Data was loaded into Microsoft Fabric’s Lakehouse via Dataflow Gen2, enabling robust transformations and semantic linking to Power BI Desktop. Data Optimization: Used Import Mode for better performance. Renamed columns, created a Date table with DAX, calculated profits, and optimized data types. Key Insights: Total Sales: Achieved $18.9 million, surpassing the $10 million KPI. Peak Year: 2013, with 46% of the total quantity ordered and the highest average revenue of $8.7 million. Product Leaders: Bikes contributed 81% of revenue, with Road Bikes leading subcategories. Top Region: The U.S. contributed 62.7% of revenue ($57 million profit), while Canada had the highest profit margin at 38%. Seasonal Trends: March 2014 accounted for 6.23% of total revenue, indicating strong sales during specific months. Recommendations: Increase Bikes inventory for peak months like March and May. Invest in regions/products with higher profit margins, e.g., Canada. Boost advertising in Australia and Germany. Study operational costs in Australia to address low profit margins.See More

Gaming Industry Analysis

Objective: Analyze the gaming industry to identify top-performing games, assess market trends, and provide actionable recommendations for stakeholders. Tools Used: Microsoft Excel (Version 2310) Power BI Desktop (Version 2.121.903.0) Power Query Editor DAX Dataset Overview: The dataset comprised a CSV file of 1.32 MB with 12 columns detailing game sales by region, platform, genre, and publisher. Data cleaning included renaming columns, fixing data types, and removing inconsistencies to achieve 100% data quality. Key Findings: 1. Top-Selling Consoles: PlayStation 2 led with $1.26 million in revenue (25.42% of total sales), followed by Xbox 360 at $979,600 and PlayStation 3 at $957,890. Xbox 360 had the highest price per unit ratio ($775), indicating strong profitability. 2. Regional Sales: North America dominated with $3.3 million (48.35% of total sales), followed by Europe, Japan, and other regions. 3. Popular Game Genres: Action games generated $1.75 million, leading in revenue, followed by Sports at $1.33 million and Shooter games at $1.04 million. Role-playing and Platform games also showed significant earnings. 4. Best-Selling Games: "Wii Sports" topped the list with $82,740 in total sales. "Grand Theft Auto V" and "Super Mario Bros." also demonstrated notable sales at $55,920 and $45,310, respectively. 5. Top Publishers: Nintendo led with $1.79 million in total sales, followed by Electronic Arts ($1.11 million) and Activision ($727,110). 6. Industry Trends: Notable growth occurred in the early 1980s, with peak annual changes exceeding 200%. However, there were significant declines post-2016, showing the cyclical nature of the market. Recommendations: Focus Marketing on High-Yield Consoles: Invest in marketing for high price-per-unit consoles like PlayStation 2 for greater profitability. Encourage Diverse Game Development: Push publishers to create games in high-earning genres (e.g., Action, Sports) to attract a wider audience. Target Underperforming RegSee More

Adidas US Sales Analysis Dashboard

bjective: Develop an interactive Power BI dashboard to provide comprehensive insights into Adidas' sales performance across the US, focusing on product trends, regional performance, and sales channels. Key Insights: 1. Sales Overview: Total Sales: $263 million with a profit of $96 million. Total Units Sold: 533K units across various categories. Transaction Count: 1,000 transactions contributing to overall revenue. 2. Regional Performance: Top Region: The West region led with $12.3 million in sales, followed by the Midwest ($7.8 million) and Northeast ($6.9 million). Other regions like the Southeast and South contributed $6.7 million and $5.5 million, respectively. 3. Product Insights: Top Products: Men's Street Footwear was the highest seller, bringing in $9.6 million, followed by Men's Athletic Footwear and Women's Street Footwear, each generating around $6.7 million. The average price for Men's Street Footwear was $32. 4. Sales by Retailer: Leading Retailers: Walmart was the top retailer with $9.9 million in sales, followed closely by Amazon ($9.7 million) and Kohl's ($8.4 million). Other notable retailers included Sports Direct and Foot Locker. 5. Sales Methods: In-store sales dominated, totaling $4.1 million, while online and outlet channels accounted for $3.1 million and $2.4 million, respectively. 6. Monthly Trends: The analysis highlighted a significant variation in profit and sales month-over-month (MoM), with peak growth months showing over 200% increases compared to previous years. Seasonal Trends: The highest profit margins were observed in specific months, indicating potential opportunities for targeted promotions. Recommendations: Focus on High-Performing Products: Increase stock and marketing for Men's Street Footwear and high-demand regions like the West. Enhance Online Presence: With significant sales through online channels, boosting digital marketing could drive further growth. Regional Marketing Strategies: Tailor campaigns for the Midwest See More

House Price Prediction in Nigeria

Objective: Develop a predictive model to estimate house prices in Nigeria, assisting stakeholders such as real estate developers, buyers, and policymakers with data-driven decision-making. Methodology: Data Collection: The dataset comprised multiple property features including location, size, number of rooms, and amenities. Data Preprocessing: Data cleaning involved handling missing values, normalizing numerical features, and encoding categorical variables to ensure accurate model training. Model Development: Implemented various machine learning algorithms, including Linear Regression, Decision Trees, and Random Forest, to identify the best-performing model for house price prediction. Performance metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) were used for evaluation. Feature Engineering: Extracted significant features like proximity to business districts, property type, and age of the building, which had a strong correlation with price variations. Key Findings: Top Predictors: Location and property size were the most influential factors, with prime areas in Lagos and Abuja showing higher price predictions. Model Performance: The Random Forest model outperformed other algorithms with the lowest MAE and RMSE, making it the most reliable for predicting house prices. Recommendations: Data Enrichment: Collect more granular data on additional variables such as market trends and neighborhood safety to improve prediction accuracy. Application: Utilize this model for price setting by real estate developers and for prospective buyers to benchmark offers. Periodic Updates: Regularly update the model with new data to maintain accuracy amid changing market conditions. This project highlights the use of machine learning to bring transparency and efficiency to the Nigerian real estate market, helping stakeholders make informed choices based on robust data analysis.See More

Work History

Data Analyst

DatoreEnhanced data accuracy by 35% through meticulous data cleansing and modeling techniques, transitioning energy and event data from PowerPoint to Excel, ensuring stakeholders make informed decisions based on precise insights. Boosted data integrity by 30% using Sisense and AWS Redshift to manage energy data, and accelerated Excel exports by 50%, enhancing analytical and investigative efficiency, saving time and reducing costs. Reduced downtime by 25% through advanced statistical analysis and trend identification, enabling swift identification and repair of malfunctioning energy tracking devices, thereby optimizing productivity and maintenance efficiency. Utilized Power Query (M) language for advanced data cleaning and transformation, ensuring team members have access to highly accurate data, allowing them to focus on other business tasks and reducing the stress of dealing with lengthy, unformatted datasets. Improved user experience by troubleshooting and creating dashboards, enhancing data interaction efficiency by 40%. Aligned data transformation with business objectives, providing precise insights and boosting decision-making efficiency by 35%.

Data Science Lead

ATC AfricaCreated and presented monthly reports and dashboards using Power BI and Excel, providing key insights on over 200 member registrations and their feedback to stakeholders, ensuring informed decision-making based on accurate data. Conducted survey analytics and collaborated on database requirements to enhance the community registration portal, leveraging systematic data transformation and best practices. Implemented sentiment analysis in Power BI, utilizing key phrase extraction and sentiment scoring, leading to a 60% improvement in decision-making by providing precise insights. Organized and led a 3-day bootcamp for a diverse group of learners from three African countries, fostering a dynamic exchange of ideas and enhancing their experience with the analytics solution.

Research Assistant

Blitz VenturesFeb 2021 - Jan 2024 • 3 yrsStreamlined the production of comprehensive research reports using Microsoft Word, reducing weekly manual work by 10 hours and ensuring stakeholders make informed decisions based on accurate data. Improved communication and planning efficiency by 35% through advising clients on a wide range of research methods, leveraging systematic data transformation and best practices. Achieved 95% improvement in accuracy by utilizing advanced statistical methods, including hypothesis testing with charts and tables, to analyze data and draw precise research conclusions. Revealed valuable patterns and correlations by applying statistical expertise using tools such as SPSS and Excel, ensuring stakeholders have access to highly accurate data. Fostered a dynamic exchange of ideas for innovative research methodologies by collaborating closely with diverse clients to understand project requirements. Enhanced and refined research manuscripts by conducting meticulous reviews and providing constructive feedback and strategic insights. Streamlined research processes through data-driven optimizations, enhancing efficiency and precision. Ensured alignment between research objectives and analytical approaches by maintaining ongoing client communication. Transformed raw data into meaningful research insights by utilizing advanced statistical software for rigorous analyses. Communicated complex analysis outcomes and actionable recommendations to clients through engaging reports and presentations, ensuring a seamless and efficient interaction with data for better decision-making.

Data Analytics Quantium Virtual Experience Program / Forage

QuantiumJun 2021 - Apr 2022 • 11 mosUtilized Python for data analysis and manipulation, ensuring stakeholders make informed decisions based on accurate data. Analyzed clients transaction dataset, extracting insights on customer purchasing behaviors and providing strategic commercial recommendations, leveraging systematic data transformation and best practices. Employed advanced data analytics techniques to reveal significant patterns and trends, contributing to business growth and profitability through precise insights. Conducted comprehensive benchmark store analysis, assessing the impact of trial store layouts on customer sales and delivering actionable insights for optimizing store performance. Created visually compelling data visualizations and reports to communicate findings, aiding data-driven decision-making and enhancing user experience with the analytics solution.

Education

Olabisi Onabanjo University(O.O.U)

Bachelor of Science - BS, Business Administration and Management, General2017 - 2021

Corporate Finance Institute® (CFI)

Business Intelligence and Data Analytics (BIDA) | Financial Modelling and Valuation Analyst2023

Microsoft Learn

Microsoft Technologies and Continuous Learning

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