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


* Actual course outline may vary depending on offering center. Contact your sales representative for more information.

Learning Objectives

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

  • Data Preparation and Cleaning

  • Data Models and Structured Data
    Data Manipulation

  • Data Exploration and Visualization

  • Identifying the Right Attributes
    Generating Targeted Insights
    Visualizing Data

  • Unsupervised Learning- Customer Segmentation

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

  • Choosing the Best Segmentation Approach

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

  • Predicting Customer Revenue Using Linear Regression

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

  • Other Regression Techniques and Tools for Evaluation

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

  • Supervised Learning- Predicting Customer Churn

  • Classification Problems
    Understanding Logistic Regression
    Creating a Data Science Pipeline

  • Fine-Tuning Classification Algorithms

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

  • Modeling Customer Choice

  • Understanding Multiclass Classification
    Class Imbalanced Data


Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts. It'll help if you have prior experience of coding in Python and knowledge of high school level mathematics. Some experience with databases, Excel, statistics, or Tableau is useful but not necessary.




While there are no prerequisites for this course, please ensure you have the right level of experience to be successful in this training.


Length: 3.0 days (24 hours)


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