CourseInfo | SimpliTrain

Data Science for Marketing Analytics

Learning plan iconE-Learning

Description

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

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.

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

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.

Lesson Id Title Description
1 Data Preparation and Cleaning Data Models and Structured Data
pandas
Data Manipulation
2 Data Exploration and Visualization Identifying the Right Attributes
Generating Targeted Insights
Visualizing Data
3 Unsupervised Learning: Customer Segmentation Customer Segmentation Methods
Similarity and Data Standardization
k-means Clustering
4 Choosing the Best Segmentation Approach Choosing the Number of Clusters
Different Methods of Clustering
Evaluating Clustering
5 Predicting Customer Revenue Using Linear Regression Understanding Regression
Feature Engineering for Regression
Performing and Interpreting Linear Regression
6 Other Regression Techniques and Tools for Evaluation Evaluating the Accuracy of a Regression Model
Using Regularization for Feature Selection
Tree-Based Regression Models
7 Supervised Learning: Predicting Customer Churn Classification Problems
Understanding Logistic Regression
Creating a Data Science Pipeline
8 Fine-Tuning Classification Algorithms Support Vector Machine
Decision Trees
Random Forest
Preprocessing Data for Machine Learning Models
Model Evaluation
Performance Metrics
9 Modeling Customer Choice Understanding Multiclass Classification
Class Imbalanced Data