Extracted orders data for an Ecommerce business using the Kaggle API into a Pandas dataframe. Performed data cleaning and transformation in Python using various string and datetime functions available within Pandas. Loaded the cleaned dataset to a MySQL database. Used SQL to draw insights on highest revenue generating
products, highest selling products in each region, growth by profit among product categories in 2023 as compared to 2022
and month over month growth comparison in sales between 2022 and 2023 for the buisness.
Used advanced SQL to solve real world business problems for a product based company such as payment funnel analysis, calculating MoM revenue for the company, flagging upsell oppurtunities and tracking subscription cancellation reasons among others.
Assisting "PhoneNow", a telecom client with BI tasks such as presenting call centre trends and performing customer churn analysis.
Manipulated data of a ficticious bank in Power BI to relate customer churn rates across various age groups, tenure, region and card types in order to drive future strategies for customer retention.
Pulled sales data of a fictional E-commerce store and presented key metrics such as the average order value, monthly profits, revenue by region, top customers and the proportion of different payment methods used for transactions etc. in a Power BI dashboard.
In this project we will be guiding an aspiring app developer to build an app for the Apple store by analyzing a dataset providing app listings,unique genres, user ratings and app descriptions within SQL .
A Tableau dashboard created from a dataset pertaining to Airbnb property listings in Seatle US. Presented insights on key metrics such as revenue throughout the entire year, property prices by zip codes and the number of property listings offering single or multiple bedroom options in the area.
A Power BI dashboard analyzing results from a dataset of a survey completed by 600+ participants associated with the "data" domain either as working professionals or students.
Used SQL to clean a dataset pertaining to properties in the Nashville area of UNited States.Removed duplicate values, filled missing values, split property and owner addresses based on zipcode, city and state and added markers for sold and currently available properties etc.
Used SQL to explore a global dataset pertaining to the COVID-19 outbreak. Extracted insights such as the percentage of deaths per COVID cases IN Pakistan, countries with the highest instance of COVID cases and countries with the highest death counts per population etc.