Training on Mastering Exploratory Data Analysis with Python

Module 1: Introduction to Exploratory Data Analysis

• Purpose and process of EDA
• Why EDA is critical in data science and analytics
• Key Python libraries: NumPy, Pandas, Matplotlib, Seaborn
• Case Study: How EDA improved model accuracy in a customer churn project
• Practical Exercise: Perform basic EDA on a sample dataset

Module 2: Data Import and Exploration

• Loading data from CSV, Excel, SQL databases, and APIs
• Initial exploration with Pandas: head, describe, info
• Understanding data types, structures, and metadata
• Case Study: Identifying data structure issues in a retail dataset
• Practical Exercise: Import and explore a multi-source dataset

Module 3: Data Cleaning and Preprocessing

• Handling missing values through deletion or imputation
• Detecting and addressing outliers
• Normalization, standardization, and basic feature engineering
• Case Study: Cleaning and preparing health survey data for analysis
• Practical Exercise: Clean raw data and prepare it for analysis

Module 4: Univariate Analysis

• Summary statistics: mean, median, quartiles, skewness, kurtosis
• Visualizing distributions: histograms, boxplots, density plots
• Exploring categorical variables using frequency and proportions
• Case Study: Distribution analysis for e-commerce purchase amounts
• Practical Exercise: Generate univariate visualizations

Module 5: Bivariate Analysis

• Correlation analysis (Pearson, Spearman)
• Scatter plots and relationship exploration
• Crosstabs, pivot tables, and group comparisons
• Hypothesis testing: t-test, chi-square
• Case Study: Understanding customer behavior drivers
• Practical Exercise: Conduct bivariate comparisons on dataset

Module 6: Multivariate Analysis

• Pair plots and correlation matrices
• PCA for dimensionality reduction
• Clustering techniques: k-means, hierarchical clustering
• Case Study: Segmenting customers using multivariate data
• Practical Exercise: Perform PCA and cluster analysis

Module 7: Data Visualization

• Core visualization techniques: bar, line, scatter, heatmaps
• Improving clarity and aesthetics with Matplotlib/Seaborn
• Interactive visualizations using Plotly
• Case Study: Building visual dashboards for management reporting
• Practical Exercise: Create an interactive visualization dashboard

Module 8: Time Series Analysis

• Exploring trends, seasonality, and residuals
• Time series decomposition
• Basic forecasting: ARIMA, exponential smoothing
• Case Study: Forecasting daily sales for a retail company
• Practical Exercise: Analyze and forecast a time series dataset

Module 9: Case Studies & Real-World Applications

• Working with financial, customer, and social media datasets
• Using EDA to diagnose real business issues
• Turning insights into actionable recommendations
• Case Study: EDA for customer sentiment and market insights
• Practical Exercise: Complete end-to-end EDA on a real dataset

Module 10: Advanced Topics (Optional)

• Geospatial data exploration using GeoPandas
• Text mining and NLP-based exploratory techniques
• Integrating EDA with machine learning pipelines
• Case Study: Mapping geospatial trends in public health data
• Practical Exercise: Conduct EDA on text or geospatial data