This portfolio contains four projects, each based on the skills used for analyzing data in the field of Data Analysis and other similar data-centric professions.
In this comprehensive project, I harnessed the power of Excel, a widely-used data analysis tool, to dissect and interpret Adidas US Sales data for 2020. The project is structured into four distinct worksheets within a single workbook:
Presents the original sales data, serving as the foundational dataset for the analysis.
Enhances the original data with Excel’s advanced features and formulas, extracting key metrics such as the list of retailers, product types, operating profits, total units sold, and sales methods.
Features pivot tables that crystallize data patterns, facilitating a deeper understanding of the sales figures.
Deploys dashboards that vividly illustrate critical insights, including retailer-based unit sales, profitability by retailer, the most lucrative regions, city-wise unit sales, and product share contributions.
"This project not only showcases the versatility of Excel in managing and analyzing data but also underscores the strategic use of visual tools to uncover actionable business insights."
In this project, I employed SQL and MySQL to manipulate a CSV dataset of mobile data sourced from a public community. The project encompassed several stages:
Utilized DROP statements to eliminate extraneous records, streamlining the dataset for clarity and simplicity. Renamed columns for more intuitive access and manipulation through SQL queries.
Performed data cleansing to eradicate anomalies such as unwanted symbols and words, converting them into standardized data types. Additionally, introduced new columns to facilitate a more straightforward data comprehension.
Applied SQL clauses like SELECT, WHERE, ORDER BY, and LIKE to extract vital information, enabling analyses such as:
- Identifying highly-rated mobiles with substantial rating counts.
- Locating the most expensive Apple mobiles.
- Discovering mobiles with terabyte storage in black color.
- Searching for mobiles with dual front cameras.
- Filtering mobiles within the price range of ₹10,000 to ₹20,000.
"This project demonstrated the robust capabilities of SQL in data cleaning, transformation, and analytical querying, leading to actionable insights."
In this project, I utilized Power BI, a leading data visualization tool, to transform a CSV dataset into a compelling dashboard that provides clear insights. The project involved:
Commenced with importing a comprehensive CSV dataset. Implemented card visuals for displaying key metrics such as customer count, phone service subscriptions, and total revenue.
Utilized clustered bar charts for demographic analysis, including customer demographics, preferred payment methods, and internet service types. Employed map visuals to illustrate the geographic distribution of customers. Created funnel visuals to depict the customer journey from acquisition to churn. Analyzed contract types with a donut chart to highlight prevalent contracts.
Developed a tree map visual to provide a clear view of the primary reasons for customer churn, offering insights into customer needs and service gaps.
"This project showcased the analytical capabilities of Power BI and also emphasized the importance of visual storytelling in understanding customer behavior and driving business decisions."
In this project, I utilized the Jupyter Notebook, a popular data science tool, to conduct an analysis. The workflow included:
Employed the Beautiful Soup library to extract data from a specified URL.
Used the Pandas library to clean and structure the data for analysis.
Created insightful visualizations using Matplotlib, including:
- A bar chart showcasing the ‘Top 10 countries by population’.
- Subplots comparing ‘Population change trends’ across various countries.
- A line plot for the ‘Top 10 countries by land area’.
- A pie chart illustrating the ‘World share percentage based on land area’ of the top 10 countries.
- A scatter plot correlating ‘Fertility rate and population’ figures.
“This project highlighted the powerful capabilities of Jupyter Notebook in data scraping, manipulation, and visualization, culminating in actionable insights into demographic patterns.”