• Cleaned, merged, and reshaped tabular data using pandas, then visualized trends in sales, city-level infections, and market returns via stacked bar charts, line graphs, pie charts, and histograms with matplotlib.pyplot
• Conducted financial time series analysis using pandas and scipy.stats, including return calculation, distribution diagnostics (skewness & kurtosis), and outlier detection based on standard deviation thresholds
• Performed natural language processing using nltk and Textblob, including word tokenization, stopword filtering, Part-of-Speech tagging, stemming, frequency plotting, and sentiment scoring via Valence Aware Dictionary and sEntiment Reasoner (VADER)