Duration
Python for Data Exploratory Analysis
2 days
Day 1: Foundations of Data Exploration and Visualization
Introduction to Data Exploration (Morning Session)
- Welcome and Course Overview
- Importance of Data Exploration in Data Science
- Role of Python in Data Exploration
- Setting Up the Python Environment
- Installing Python and Data Science Libraries (NumPy, Pandas, Matplotlib, Seaborn)
- Introduction to Jupyter Notebooks
- Exploratory Data Analysis (EDA) Basics
- Understanding the Dataset
- Descriptive Statistics and Summary Metrics
- Initial Data Inspection with Pandas
Data Visualization with Matplotlib and Seaborn (Afternoon Session)
- Introduction to Data Visualization
- Importance of Data Visualization in EDA
- Choosing the Right Visualization for Data Types
- Matplotlib: Creating Static Plots
- Line Plots, Scatter Plots, and Bar Plots
- Customizing Plot Appearance
- Seaborn: Statistical Data Visualization
- Enhancing Data Visualizations with Seaborn
- Heatmaps, Pair Plots, and Categorical Plots
- Hands-On Exercise: Exploratory Data Visualization
- Visualizing Dataset Patterns and Relationships
- Creating Customized Plots for Insightful Exploration
Day 2: Advanced Data Exploration Techniques
Advanced Data Manipulation with Pandas (Morning Session)
- Data Cleaning and Preprocessing
- Handling Missing Data
- Data Imputation Techniques
- Outlier Detection and Handling
- Advanced Pandas Operations
- Grouping and Aggregating Data
- Merging and Joining Datasets
- Pivot Tables and Cross-Tabulations
Time Series Analysis and Interactive Visualization (Afternoon Session)
- Time Series Data Exploration
- Understanding Time Series Data
- Time Series Plotting and Analysis with Python
- Interactive Data Visualization with Plotly
- Introduction to Plotly
- Building Interactive Visualizations
- Hands-On Exercise: Time Series Analysis and Interactive Visualization
- Analyzing Time Series Patterns
- Creating Interactive Plots with Plotly
Programme scope, delivery arrangements and suitability are confirmed during enquiry.