Experienced data analyst with expertise in data visualization, statistical analysis, and data cleaning. Proficient in Tableau, Python, SQL, and Excel. Proven ability to create insightful data visualizations and communicate complex data insights to stakeholders. Completed several successful projects, including a COVID-19 Vaccination Dashboard, Personal Finance Dashboard, Data Cleaning using SQL, Bike Sales Dashboard, Amazon Web Scraper, and Twitter Sentiment Analysis.
● Designed and created a COVID-19 Vaccination Dashboard using Tableau that provided critical insights into the progress of the vaccination effort.
● Analyzed data on the number of doses administered, the number of people vaccinated, and other relevant metrics.
● Created visualizations such as time series analyses, age group breakdowns, and demographic data.
● Designed and created a Personal Finance Dashboard using Python that provides insights into personal finance such as budgeting, spending analysis, and investment tracking.
● Analyzed data on personal finance using Python libraries such as Pandas and Matplotlib.
● Created visualizations such as income vs expenses, monthly budget, and net worth.
● Communicated findings to users for better financial planning.
● Cleaned data using SQL by removing duplicates, missing data, and incorrect data.
● Ensured data quality by checking data consistency and data integrity.
● Improved data accuracy and made it ready for data analysis.
● Designed and created a Bike Sales Dashboard using Excel that provided insights into bike sales trends.
● Analyzed data on bike sales using Excel functions such as pivot tables and charts.
● Created visualization.
● Created an Amazon Web Scraper using Python that extracts data from the Amazon website and converts it into a CSV file for data analysis.
● Extracted data such as product name, price, rating, and review count.
● Improved efficiency of data collection and made it more automated.
● Designed and created a Twitter Sentiment Analysis using Python that analyzed tweets and classified them as positive, negative, or neutral.
● Analyzed data on tweets using Python libraries such as TextBlob and Pandas.
● Created visualizations such as word clouds, sentiment distribution, and top positive/negative words.