Course Outline

Module 1: Introduction to Python for Customer Analytics

• Overview of Python and its applications in customer analytics
• Setting up the Python environment
• Introduction to Python libraries: Pandas, NumPy, Matplotlib
• Data types, variables, and basic operations in Python
• Case Study: Analyzing customer demographics data using Python basics.

Module 2: Data Cleaning and Manipulation

• Importing customer data from various sources
• Handling missing values and data inconsistencies
• Data transformation and normalization techniques
• Merging and concatenating datasets
• Case Study: Preparing a raw customer dataset for analysis by cleaning and transforming the data.

Module 3: Exploratory Data Analysis (EDA)

• Descriptive statistics and summary measures
• Visualizing customer data with Python
• Identifying patterns, trends, and outliers
• Customer segmentation through clustering techniques
• Case Study: Conducting EDA on customer purchase data to identify key customer segments.

Module 4: Predictive Modeling and Machine Learning

• Introduction to machine learning algorithms for customer analytics
• Building regression models to predict customer behavior
• Classification techniques for customer segmentation
• Evaluating and tuning model performance
• Case Study: Developing a predictive model to forecast customer churn.

Module 5: Data Visualization and Reporting

• Creating effective visualizations with Matplotlib and Seaborn
• Communicating insights through dashboards and reports
• Integrating customer analytics findings with business strategies
• Automating report generation using Python scripts
• Case Study: Building a dashboard to visualize customer insights and present findings to stakeholders.