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Ntombiyokuthula Innocentia Khwela

Data Analyst at Boleng Business Solutions

Data Science at ALX

South Africa

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Hi, I'm Ntombiyokuthula Innocentia Khwela!

Data Analyst at Boleng Business Solutions

Results-driven Data Analyst with over 5 years of experience in analysing, interpreting, and reporting data to drive business decisions. Proficient in SQL, SAP, Excel, and data visualization tools, with a strong ability to clean, transform, and present data in actionable formats. Recognized for optimizing data processes, improving reporting efficiency, and delivering insights that enhance business performance. Excellent communicator with a passion for continuous learning and delivering high-quality data insights to stakeholders.

Socials

Experience

Education

Certificates & Badges

No certificates or badges added

Projects

Bike Buyers Project

https://github.com/Ntombi-the-analyst

Data Analyst

Dataset is from Youtube. This project will demonstrate my analytical skills in customer segmentation, trend analysis, and dashboard creation, emphasizing my ability to translate complex data into actionable insights for business decisions. Project Title: Bike Buyers Analysis.

Overview: The "Bike Buyers Analysis" project focuses on identifying patterns and factors influencing bike purchases among customers. By leveraging data analysis and visualization, this project helps in understanding customer segments, purchasing behavior, and demographic characteristics, supporting strategic decisions on customer targeting and product offerings.

Project Goals: Customer Segmentation: Classify customers by demographics such as age, income, marital status, and car ownership. Purchase Analysis: Examine factors that correlate with bike purchases, including income, region, commute distance, and family structure. Visual Reporting: Summarize insights through visual dashboards for easy interpretation and strategic decision-making.

Key Methodologies: Data Cleaning and Transformation: Organized data from raw form into structured insights, ensuring accuracy and relevance. Descriptive Analytics: Analyzed demographic data to find trends, such as the percentage of customers with bikes, purchasing tendencies by gender, and impact of income on bike purchases. Customer Segmentation Analysis: Grouped customers by attributes (e.g., age group, marital status) to highlight target demographics. Dashboards and Reports: Created visual summaries that display overall purchasing trends, customer profiles, and regions with higher purchase rates.

Tools and Technologies: Data Processing: Excel for data organization and calculations. Visualization: Excel Dashboards to display insights on buyer profiles and purchasing patterns. Data Structuring: Used SQL concepts in hypothetical migration planning for more robust data handling.

Key Insights: Purchase Patterns: Observed specific demographics (like income level, car ownership) that show higher bike purchase rates. Target Segments: Identified top-earning customer segments and regions with high buying potential. Operational Recommendations: Suggested customer segmentation for targeted marketing based on statistical insights.

Languages

English

Native

Skills

Data Analysis

Microsoft Excel

Data Governance

Data Modeling

Data Manipulation

Power BI

Microsoft SQL

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