This Competitive Market Analysis Report offers a comprehensive overview of the market landscape,
focusing on the top 5 manufacturers: VanArsdel, Abbas, Aliqui, Currus, and Natura.
It features detailed sales data across various channels, regional performance insights, and sentiment analysis.
Discover key trends and strategic opportunities to enhance your business decisions.
This project was developed as part of my BI Academy training program, where I gained expertise in SQL, SSMS, and Power BI.
As the final project of the program, it demonstrates my ability to apply these skills in creating comprehensive market analysis
and insightful business intelligence reports.
In my recent project with the Layoffs_2022 dataset from Kaggle, I used SQL to clean the data by handling missing values, standardizing formats, and removing duplicates. After the data was prepared, I performed exploratory data analysis (EDA) to uncover trends and patterns in layoff activities. This involved analyzing key metrics, visualizing industry impacts, and identifying significant trends, which provided valuable insights into the state of the job market in 2022.
In this project, I worked with the **Bike Buyers** dataset from Kaggle to enhance data quality and provide actionable insights. I performed extensive data cleaning in Excel, addressing missing values, correcting inconsistencies, and standardizing data formats. Following this, I developed an interactive dashboard that allows users to explore key trends and patterns in bike purchasing behavior. The dashboard features dynamic charts and filters, enabling a detailed analysis and visualization of the data. This project not only showcased my ability to refine and manage datasets but also demonstrated my skills in creating interactive tools for data-driven decision-making.
In this project, I utilized Python and Jupyter Notebooks to develop a web scraping tool for Amazon. The script extracts product information, such as prices, ratings, and reviews, from various product pages. By automating data collection, I efficiently gathered valuable insights for analysis. This project demonstrates my skills in Python programming, data extraction, and the practical application of web scraping techniques.
The Google Data Analytics Capstone Project focused on analyzing bike share data to uncover usage patterns and provide actionable insights.
The analysis aimed to determine how factors such as time of day, weather conditions, and location affect bike share usage.
Key findings included peak usage times during weekday mornings and evenings, with a notable decline in usage during adverse weather conditions
like rain and extreme temperatures. Additionally, the study identified that bike stations in central urban areas and near transit hubs were
highly popular, whereas underserved neighborhoods lacked adequate bike availability. Based on these insights, recommendations were made to
adjust bike distribution according to weather forecasts, improve station coverage in high-demand areas,
and enhance bike rebalancing strategies to better meet user needs.