Data Analysis Projects in Gauteng
Data Analysis Projects in Gauteng
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Benjamin van der Merwe
Forex Dashboard Web Application
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9
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Thabo Mailula
Customer & Marketing Insights Dashboard | Power BI Project This Power BI project analyzes customer behavior, sales performance, promotions, and product cross-sell opportunities using sample data. The dashboard provides interactive insights in four areas: KPI Scorecards – New customers, repeat purchase rate, average basket size, and customer lifetime value (CLV). Promotion Effectiveness – Revenue trends and comparison charts showing how discounts impact sales and profit. Product Affinity / Cross-Sell – Heatmaps reveal commonly purchased product combinations. Customer Retention & Cohorts – Tracks returning customers and retention patterns.
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8
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Xolani Mazibuko
Sales Register
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7
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Neo Tshili
Amazon Prime Dashboard
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24
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Pako Kole
Sentry Safe Solutions
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3
1
Christelle kalanda
Custom Dashboard
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7
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Anja Brits
Platos Pizza Sales - Power BI Project
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0
2
Usamah W Maphumulo
Behind every great dashboard is a clean query. I’m currently using SQL to transform raw event logs into actionable intelligence for an e-commerce build. Key Metrics Extracted: Traffic Quality: Identifying high-intent sources via avg_session_duration. Campaign ROI: Using expected_uplift to pinpoint winning customer segments. Behavioral Trends: Mapping friction points in the checkout funnel. Moving from "gut feelings" to real-time data in Power BI, Tableau, and Looker Studio.
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2
29
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Benjamin van der Merwe
Tradingview Custom Indicator Development
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19
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Thabo Mailula
Maji Ndogo Water Project – Part 2 | Data Analysis & Insights In Part 2 of the Maji Ndogo Water Project, I focused on cleaning and analyzing employee and water source data to understand access, detect patterns, and prioritize interventions. Key Activities: Employee Data Cleaning – Standardized emails, converted to lowercase, and updated phone numbers. Employee Performance – Counted visits per employee and identified top surveyors. Water Source Distribution – Analyzed records by town and province, calculated rural percentages, and total people served. Water Source Usage – Calculated average and total users per source type. Prioritizing Repairs – Ranked water sources by people served to guide repair priorities. This workflow highlights water access, employee activity, and priority areas for intervention.
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4
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Tumisang Kalagobe
Sales process optimisation
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12
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Neo Tshili
National Sales and Customer Ratings
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0
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Anja Brits
Game of Phones – Power BI 10th Birthday DataViz Contest
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0
1
Benjamin van der Merwe
Customer Segmentation for Targeted Marketing
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5
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Thabo Mailula
Workflow Explanation – Part 1 Here’s what I did in this first part of the Maji Ndogo Water Project: 1.Understand the Data First, I looked at the types of water sources in the dataset: rivers, wells, shared taps, home taps, and broken taps. I noted which sources are generally safe versus which are high risk. This step helped me know what “normal” data should look like. 2.Identify Anomalies in Queue Times I checked for visits where people waited more than 8 hours (500 minutes). This flagged water sources that may have access problems or data errors. 3.Check Water Quality Records for Invalid Revisits I focused on records with perfect water quality scores (10) but multiple visits. According to the survey rules, only shared taps should have multiple visits. This step identified invalid or duplicated records in the database.
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4
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Neo Tshili
Daily Sales Calendar
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13
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