Session 5: Data Visualization with Matplotlib & Seaborn

• Creating basic plots: Line, bar, histogram, scatter, etc.
• Customizing plots: Titles, labels, legends, colors
• Creating histograms, box plots, and heatmaps
• Hands-on Exercise:
  • Creating line and bar plots from datasets
  • Creating a heatmap to visualize correlations

Session 6: Statistical Analysis & Hypothesis Testing

• Descriptive statistics: Mean, median, mode, variance, standard deviation
• Probability distributions: Normal, Binomial, Poisson distributions
• Inferential statistics: Confidence intervals, p-values
• Introduction to hypothesis testing
• Types of hypothesis tests: T-test, Chi-squared test, ANOVA
• Hands-On Exercise:
  • One-Sample t-Test, Chi-Square Test, ANOVA using SciPy
  • Performing hypothesis testing on a real-world dataset

Session 7: Optimizing Data Frames using Vectorized Operations

• Using apply() vs. vectorized operations
• Performance considerations when working with large datasets
• Hands-on Exercise: Optimizing code performance using vectorization

Final Project: Real-World Data Analytics Case Study

• Working with a real-world dataset
• End-to-End Data Analysis Process: Cleaning, manipulation, visualization, and hypothesis testing
• Hands-on Exercise:
  • Apply learned concepts to analyze and visualize business data