Data Science for Marketing Analytics

Data Science for Marketing Analytics covers every stage of data analytics, from working with a raw dataset to segmenting a population and modeling different parts of it based on the segments.

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 chapters, 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 chapters, 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.

By the end of this course, you will be able to build your own marketing reporting and interactive dashboard solutions.

What you will learn

• Analyze and visualize data in Python using pandas and Matplotlib
• Study clustering techniques, such as hierarchical and k-means clustering
• Create customer segments based on manipulated data
• Predict customer lifetime value using linear regression
• Use classification algorithms to understand customer choice
• Optimize classification algorithms to extract maximal information

Hardware requirements

For an optimal student experience, we recommend the following hardware configuration:

• Processor: Dual Core or better
• Memory: 4 GB RAM
• Storage: 10 GB available hard disk space

Software requirements

You’ll also need the following software installed in advance:

• Any of the following operating systems: Windows 7 SP1 32/64-bit, Windows 8.1 32/64-bit, or Windows 10 32/64-bit, Ubuntu 14.04 or later, or macOS Sierra or later.
• Browser: Google Chrome or Mozilla Firefox.
• Conda
• Python 3.x