The PQPlus project aims to provides insights into the training in Data Analysis, Cybersecurity, and UI/UX design over the past four years. by analyzing various aspect of revenue and candidates. It involves tracking daily, monthly, and yearly revenue, total registrations, total activation, and leads across the different locations. we seek to identify trends, patterns, make data driven recommendations and gain deeper understanding of the business performance.
The primary dataset used for this analysis is the PQPlus Url link for each course product registration for the Abuja and Lagos location, containing detailed information about and the appended dataset combining information from eight existing training programs in Lagos and Abuja:
-- Lagos Data Analyst Weekend - Data on weekend training programs. -- Lagos Data Analyst Midweek - Data on weekday training programs. -- Lagos UI/UX - Data on UI/UX training programs. -- Lagos Cyber Security - Data on cybersecurity training programs. -- Abuja Data Analyst Midweek - Data on midweek training programs. -- Abuja Data Analyst Weekend - Data on weekend training programs. -- Abuja Cyber Security - Data on cybersecurity training programs. -- Abuja UI/UX - Data on UI/UX training programs.
POWER BI (For data cleaning, data modelling, and visualization)
EDA was conducted to identify patterns, trends and uncover insights relevant to the project. The following EDA was done on the dataset:
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Data Cleaning and Preparation: -- Remove unwanted columns and duplicate values. -- Check for and handle missing values. -- Identify and address any data inconsistencies or errors. -- Transform data into appropriate formats for analysis. -- Append.
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Descriptive Statistics: -- Calculate key variables like revenue, total number of registered candidates, total number of activated candidates, conversion ratio, average registered and activated candidates etc. -- Visualize data using guage, cards, bar clustered charts, and other relevant charts to understand the distribution of variables.
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Course product Identification: -- Analyze course product data for different location, manager, and training type.
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Revenue Breakdown: -- Analyze revenue generation by existing mangaer per training center locations. -- Identify course performance location. -- Analyzing the sales cycle revenue -- Identify yearly, monthly, daily revenue.
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Correlation Analysis: -- Explore potential correlations between program type, location, demographics, and revenue.
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Visualization and Data Modelling: -- Create clear and informative visualizations to communicate key findings to stakeholders.
Our analysis revealed the following key insights: -- PQPlus has demonstrated consistent revenue growth over the past several years, with a significant peak in 2023. -- The Data Analyst course is the top revenue generator and boasts the highest number of activated candidates. -- The Abuja location outperforms Lagos in both revenue generation and the number of registered and activated candidates. -- Sales team member Mr. Kezi has achieved remarkable success, generating ₦59 million in revenue and securing the highest number of registered and activated candidates.
Based on these findings, we recommend the following actions: -- Capitalize on end-of-year sales trends: Increase marketing and promotional efforts during October, November, and December, as well as May, to leverage historically higher sales volumes. -- Diversify course promotion: Expand promotional activities for other courses to capitalize on potential revenue streams. -- Implement targeted customer segmentation: Develop a customer segmentation strategy to effectively target high-lifetime-value (LTV) candidates.
To ensure the accuracy of my analysis, I converted all blank cells to zero for numerical (both whole number and decimal) data types. The presence of blanks would have otherwise skewed the results. While this conversion addressed a major issue, a small number of outliers remain and may still have a minor influence on the overall findings.
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