Sithandilizwe Sly Malunga
Data Analyst
Technical Skills: Python, R, SQL, Microsoft Power BI, Tableau, Microsoft Azure Machine Learning, Exploratory Data Analysis, Statistical Modelling
Education
-
B.Sc(Hons)- Mechanical Engineering University of Zimbabwe (2003) -
Programme in Advanced Strategic Management University of South Africa (2016) -
Advanced Programme in Risk Management University of South Africa (2016) - Certificate in Business Accounting Chartered Institute of Management Accountants (2015)
- Microsoft Certified Power BI Data Analyst ( Credential No. 807E4BC56B0BBDB5, Certification number: F7483C-85E546)
- Microsoft Certified Azure Data Scientist Associate ( Credential No. Pending - Exam Date - 28 January 2025 )
Work Experience
Data Analyst (Consultant) - Centre for Management & Innovation South Africa (January 2015 - Present)
- Client-Centric Data Analysis and Reporting - Conducted comprehensive data analysis for various client projects, transforming raw data into actionable insights to support strategic decision-making. This includes the preparation of dynamic dashboards, visualisations, and detailed reports using tools like Power BI, Tableau, and Excel.
- Custom Data Solutions for Diverse Industries - Collaborated with clients across multiple sectors to understand their unique challenges and design customised data solutions. These solutions include developing KPIs, building predictive models, and optimising business processes through advanced data analytics.
- Data-Driven Strategy Development - Work closely with CMISA’s consultants to integrate data insights into broader business strategies. This involves supporting client engagements by validating hypotheses, identifying trends, and offering recommendations that align with organisational goals and objectives.
Projects
1. Improving Marketing Effectiveness for ArchAngel Sports with Power BI, Python, SQL
Introduction
ArchAngel Sports, an online retail platform, has been experiencing significant challenges in sustaining customer engagement and achieving satisfactory conversion rates, despite notable investments in its marketing strategies. The business has observed declining customer interactions, reduced purchase conversions, and increasing marketing costs that do not yield proportional returns.
1. Project Objectives
The project focuses on addressing the following objectives:
- Increase Conversion Rates: Analyse the customer journey to identify drop-off points and propose actionable solutions to reduce them.
- Enhance Customer Engagement: Evaluate marketing content performance to understand what resonates most with ArchAngel Sports’s audience.
- Improve Customer Feedback Scores: Use sentiment analysis to highlight recurring pain points and positive themes in customer feedback.
To view project documentation please go to ArchAngel Sports
2. Scraping and Profiling Data of Top 100 African Companies from Wikipedia with Python
Introduction
This project demonstrates web scraping and data profiling techniques by extracting data of the Top 100 African Companies from Wikipedia. Using Python, a script was developed to automate the scraping process and generate detailed exploratory insights into the dataset. The project showcases proficiency in leveraging tools like BeautifulSoup, requests, and pandas-profiling to transform unstructured web data into actionable insights.
Key deliverables include:
- A cleaned dataset in CSV format.
- A comprehensive data profiling report to guide further analysis.
This project emphasizes the power of automation and data-driven analysis, providing a foundation for tasks such as business intelligence and market research.
Project Objectives
- Automate Data Extraction:
- Develop a Python script to scrape the Top 100 African Companies table from Wikipedia.
- Save the extracted data in a structured CSV format.
- Generate Exploratory Data Insights:
- Use
pandas-profilingto create a detailed profiling report highlighting key data patterns and attributes.
- Use
- Demonstrate Workflow Efficiency:
- Ensure the process is reproducible, efficient, and well-documented for ease of use by others.
- Practice and Showcase Skills:
- Web scraping with Python libraries (
BeautifulSoup,requests). - Data cleaning, transformation, and profiling using
pandasandpandas-profiling. - Problem-solving through debugging and HTML structure analysis.
- Web scraping with Python libraries (
To view project documentation please go to Top 100 African Companies
3. Industry Performance Analysis for African Countries with Microsoft Excel Power Query & Tableau Desktop Public
Executive Summary
This project showcases an Industry Performance Analysis Dashboard for African countries , designed to empower decision-making by presenting key performance metrics for various industries. The dashboard provides practical insights for potential investors, allowing them to evaluate industry trends and performance to inform investment strategies.
Project Overview
The dashboard focuses on:
- Market Value vs. Revenue Correlation: Highlights the relationship between market value and revenue across sectors,industries and companies.
- Performance Highlights: Visuals that showcase leading and lagging industries based on:
- Average Annual Growth.
- Compound Annual Growth Rate (CAGR) for 2, 3, and 5 years.
Objectives
- Provide a concise yet intuitive tool for evaluating industry performance trends.
- Highlight top-performing industries while identifying lagging sectors to support investment decision-making.
- Deliver an analytical framework for prioritising investment opportunities by showcasing growth consistency and market dominance.
Key Features and KPIs
- Growth Metrics: Evaluation of annual growth rates and multi-year CAGR trends.
- Market Trends: Comparative analysis of revenue contributions and market value within industries.
- Performance Distribution: Clear identification of industry leaders and underperformers.
Tools and Techniques
The dashboard was built using Tableau Desktop Public, leveraging:
- Data Visualisation to ensure clarity and impactful presentation.
- Advanced Data Aggregation to compute growth trends and comparative industry performance.
- User-Centric Design for intuitive navigation and insight discovery.
Value Proposition
The Industry Performance Analysis Dashboard acts as a strategic investment screening tool, enabling investors to focus on industries with the strongest growth potential while identifying those requiring cautious evaluation. It offers a high-level overview of key trends, supported by actionable data for portfolio planning.
This project reflects my expertise in:
- Data Analysis and Visualisation: Translating complex datasets into meaningful insights.
- Dashboard Design: Crafting professional, investor-focused dashboards.
- Strategic Thinking: Aligning data analysis with business objectives.
It stands as a prime example of my ability to bridge the gap between raw data and data-driven insights tailored for business decision-making.
To view project documentation please go to Industry Performance Analysis for African Countries
4. South African Energy Trends (2014–2023) – Power BI Dashboard
Executive Summary
This project explores South Africa’s energy generation and consumption trends and patterns from 2014 to 2023, analysing trends in primary energy generation, consumption, renewable energy adoption, and nuclear power developments among other key areas. Data was sourced from top-rated global energy databases, cleaned using Power Query in Excel, and further transformed in Power BI using DAX calculations. The final interactive dashboard, deployed on Power BI Service, provides an insightful and data-driven perspective on the country’s energy transition.
Project Overview
The project aims to uncover trends, challenges, and opportunities in South Africa’s energy sector by leveraging data analytics and business intelligence tools. It highlights the decline in total energy consumption, the growth of renewable energy, and the shifting role of coal and nuclear power in the country’s energy mix.
Objectives
- Analyse historical trends in energy generation and consumption.
- Identify key drivers behind the shift toward renewable energy.
- Evaluate the impact of coal, nuclear, and renewables on the energy landscape.
- Develop an interactive Power BI dashboard for data-driven decision-making.
Key Features & KPIs
Key Features:
- Trend Analysis – Interactive charts and visualisations to track energy changes over time.
- Energy Mix Breakdown – Insights into coal, renewables, and nuclear power contributions.
- Consumption Patterns – Year-over-year comparisons of energy demand.
Main KPIs Tracked:
- Total Energy Consumption (TWh)
- Renewable Energy Growth (MW added annually)
- Coal vs. Renewables Share (%)
- Nuclear Energy Capacity (MW)
Tools & Techniques
Tools Used:
- Microsoft Excel (Power Query) – Data wrangling, cleaning, and transformation.
- Microsoft Power BI – Data modeling, visualisation, and dashboarding.
- DAX (Data Analysis Expressions) – Advanced date table calculations and performance optimisation.
- Microsoft Power BI Service – Deployment and cloud accessibility.
Value Proposition
- Insightful Data Storytelling – Transforms raw data into meaningful, actionable insights.
- Decision-Making Support – Provides policymakers, analysts, and energy experts with data-driven intelligence.
- Showcases Technical Expertise – Demonstrates proficiency in data analytics, Microsoft Power BI, and business intelligence.
This project is a testament to my passion for data-driven decision-making and advanced analytics.