Data Analysis with Python Intermediate Tutorial

Learn an end-to-end data analysis workflow using Python, pandas, and seaborn visualization libraries in this 48-minute intermediate tutorial. Master essential data exploration techniques starting with reading CSV files and using sort_index(), then progress through creating and interpreting various visualization types including line plots, histograms, box plots, scatter plots with color-coded categories, and regression trend lines. Explore advanced seaborn features such as facet grids for split-by views, categorical plots including count plots and violin plots, distribution smoothing with KDE, and professional chart finishing techniques including proper labels, titles, style adjustments, and figure saving with appropriate DPI settings. Work within JupyterLab environment to organize your analysis workflow while reinforcing critical analytical concepts like correlation versus causation and proper chart interpretation. Gain practical skills in examining dataset structure, data types, and summary statistics, then build presentation-ready visualizations using both pandas and seaborn libraries with techniques like relational plots, bar plots with mean estimators, and frequency analysis of categorical variables.