This repository contains a collection of data analytics projects, each addressing a unique real-world problem through the application of data-driven methodologies. The projects span across multiple industries and data analysis domains such as social media, operations, hiring, movie ratings, and finance. Using tools like SQL, MySQL Workbench, Excel, and Exploratory Data Analysis (EDA), these projects showcase the power of data to generate meaningful insights for business growth and operational efficiency.
Project-3 and Project-6 are the most complex ones in this repository involving SQL and Excel.
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Objective: Demonstrate the complete data analytics process and its application in real-world scenarios.
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Tools: Excel
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Highlights:
- Performed various stages of the data analytics process including data cleaning, exploration, analysis, and reporting.
- Drawn meaningful insights through statistical methods and visualizations.
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Folder:
project-1-data-analytics-process/
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Objective: Analyze user interactions and engagement with Instagram to provide actionable insights for business growth.
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Tools: SQL, MySQL Workbench
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Highlights:
- Examined user behavior and engagement patterns.
- Provided key metrics to the management team to drive business decisions.
- Utilized SQL queries to uncover trends and answer specific business questions.
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Folder:
project-2-instagram-engagement-analysis/
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Objective: Understand and explain sudden changes in key business metrics (e.g., user engagement, sales) through operational analytics.
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Tools: SQL, MySQL Workbench
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Highlights:
- Investigated dips in daily user engagement and sales using SQL.
- Analyzed various operational scenarios and provided explanations to the operations team.
- Supported decision-making by identifying root causes of performance fluctuations.
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Folder:
project-3-operational-analytics/
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Objective: Analyze the company's hiring process and extract meaningful insights about workforce composition and salary distribution.
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Tools: Excel, Statistics
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Highlights:
- Analyzed gender distribution, salary patterns, and departmental breakdowns.
- Created visualizations to communicate findings effectively to HR and management.
- Used descriptive statistics to assess hiring trends and anomalies.
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Folder:
project-4-hiring-analysis/
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Objective: Perform an in-depth analysis of IMDB movies, focusing on genres, durations, languages, directors, and budgets to understand their impact on ratings and financial success.
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Tools: Excel
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Highlights:
- Conducted genre-wise analysis of IMDB scores.
- Examined relationships between movie durations, languages, and ratings.
- Explored director influence and budgetary impact on gross earnings.
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Folder:
project-5-imdb-movie-analysis/
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Objective: Understand the factors influencing loan defaults and identify key customer and loan attributes associated with default risk.
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Tools: Excel, EDA
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Highlights:
- Analyzed customer demographics and loan data to identify patterns linked to defaults.
- Applied EDA techniques to uncover the relationship between customer attributes and loan performance.
- Used visualizations and statistical analysis to present findings to the finance team.
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Folder:
project-6-loan-default-analysis/
- SQL & MySQL Workbench: Used for querying large datasets, analyzing user interactions, and answering business-specific questions.
- Excel: Employed for statistical analysis, visualizations, and exploratory data analysis in multiple projects.
- EDA (Exploratory Data Analysis): Utilized in projects to understand patterns, detect anomalies, and draw insights from raw data.
These projects provided insights into various business problems through the lens of data analytics. Each project explores different sectors, helping to understand how data can be leveraged to drive business growth, operational efficiency, and strategic decision-making.
- Instagram Engagement: How user behavior data can optimize social media platforms.
- Operational Analytics: Root cause analysis of sudden changes in business metrics.
- Hiring Trends: Insights into workforce dynamics and compensation structures.
- Movie Analytics: The role of genre, directors, and budget in movie success.
- Loan Default Prediction: Factors influencing loan defaults and customer risk assessment.
- Clone this repository:
git clone https://github.com/MSVelan/data-analytics-project-portfolio.git