Data Science for Marketing Analytics
The course starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you’ll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation. As you make your way through the course, you’ll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding sections, you’ll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you’ll apply these techniques to create a churn model for modeling customer product choices.
Course Objectives
By the end of this course, you will be able to build your own marketing reporting and interactive dashboard solutions.
Agenda
- Data Models and Structured Data
- pandas
- Data Manipulation
- Identifying the Right Attributes
- Generating Targeted Insights
- Visualizing Data
- Customer Segmentation Methods
- Similarity and Data Standardization
- k-means Clustering
- Choosing the Number of Clusters
- Different Methods of Clustering
- Evaluating Clustering
- Understanding Regression
- Feature Engineering for Regression
- Performing and Interpreting Linear Regression
- Evaluating the Accuracy of a Regression Model
- Using Regularization for Feature Selection
- Tree-Based Regression Models
- Classification Problems
- Understanding Logistic Regression
- Creating a Data Science Pipeline
- Support Vector Machine
- Decision Trees
- Random Forest
- Preprocessing Data for Machine Learning Models
- Model Evaluation
- Performance Metrics
- Understanding Multiclass Classification
- Class Imbalanced Data
FREE
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Course Type: Instructor Led