Data Science for Marketing Analytics

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Course Description

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

LEARNING OUTCOMES

• 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 maximumal information

Course Outline

Lesson 1: Data Preparation and Cleaning

• Data Models and Structured Data pandas
• Data Manipulation

Lesson 2: Data Exploration and Visualization

• Identifying the Right Attributes
• Generating Targeted Insights
• Visualizing Data

Lesson 3: Unsupervised Learning: Customer Segmentation

• Customer Segmentation Methods
• Similarity and Data Standardization
• k-means Clustering

Lesson 4: Choosing the Best Segmentation Approach

• Choosing the Number of Clusters
• Different Methods of Clustering
• Evaluating Clustering

Lesson 5: Predicting Customer Revenue Using Linear Regression

• Understanding Regression
• Feature Engineering for Regression
• Performing and Interpreting Linear Regression

Lesson 6: Other Regression Techniques and Tools for Evaluation

• Evaluating the Accuracy of a Regression Model
• Using Regularization for Feature Selection
• Tree-Based Regression Models

Lesson 7: Supervised Learning: Predicting Customer Churn

• Classification Problems
• Understanding Logistic Regression
• Creating a Data Science Pipeline

Lesson 8: Fine-Tuning Classification Algorithms

• Support Vector Machine
• Decision Trees
• Random Forest
• Preprocessing Data for Machine Learning Models
• Model Evaluation
• Performance Metrics

Lesson 9: Modeling Customer Choice

• Understanding Multiclass Classification
• Class Imbalanced Data

Learning Path

• Please contact us for more suggestions.

• Applied Data Science and Big Data Analytics
• Applied Data Science with Python
• Data Wrangling with Python
• Data Science Projects with Python

Ways to Attend

• Attend a public course, if there is one available. Please check our schedule, or register your interest in joining a course in your area.
• Private onsite Team training also available, please contact us to discuss. We can customise this course to suit your business requirements.