Welcome to my data analysis portfolio, where I combine the power of Excel, SQL, Tableau, and Python to derive valuable insights from complex datasets. With a passion for data-driven decision-making, I bring a diverse skill set that unravels patterns, uncovers trends, and weaves compelling narratives through visualizations. Through this portfolio, I invite you to explore my impactful projects, demonstrating how I use Excel to manipulate and analyze data, employ SQL to extract actionable information, leverage Tableau for intuitive visualizations, and harness Python for advanced analysis and automation.
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This project analyses the behavior of bike riders, examining their patterns based on membership, time, location, age, and day. The objective is to design a robust marketing strategy that would expand the market and drive an increase in membership. The data utilized in this project is sourced from the vibrant Kaggle community, ensuring a comprehensive and diverse dataset to draw insights from.
This project includes more than 20 different data analysis and visualization projects using Tableau
This project utilized a python based data analysis and visualization techniques to gain insights into the maximum infection rates across the globe. Specifically, this project aims to address the following questions: 1. What are the maximum infection rates observed worldwide, as well as in Italy and Germany? 2. How do the infection rates in Italy and Germany compare to the global maximum infection rates? 3. What is the impact of lockdown measures implemented in Italy and Germany on the spread of the virus? 4. How have the infection rate trends changed before and after the enforcement of lockdown measures in these countries?
This project is the continuation of the previous COVID19-Data-Analysis-Project and fundamentally focuses on visualization of the data using the plotly library
The analysis used the revised version of the stack overflow developer survey dataset 20. In the preparation stage, I removed duplicates. Missed values are replaced with mean of the variable. Then the data set was normalized. Exploratory data analysis (EDA) was conducted to analyze the data distribution , find outliers and correlation between the variables. After, EDA, I did visualization of data distribution, composition and relationship. Finally, I build dashboards which is a composition of the data in visual image which you will have the opportunity to look at this presentation.