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 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.

035453
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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.
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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

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

  • Processor: Dual Core or better
  • Memory: 4 GB RAM
  • Storage: 10 GB available space
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Lesson One: Data Preparation and Cleaning

Data Models and Structured Data

pandas

Data Manipulation

 Lesson Two: Data Exploration and Visualization

Identifying the Right Attributes

Generating Targeted Insights

Visualizing Data

 Lesson Three: Unsupervised Learning: Customer Segmentation

Customer Segmentation Methods

Similarity and Data Standardization

k-means Clustering

 Lesson Four: Choosing the Best Segmentation Approach

Choosing the Number of Clusters

Different Methods of Clustering

Evaluating Clustering

 Lesson Five: Predicting Customer Revenue Using Linear Regression

Understanding Regression

Feature Engineering for Regression

Performing and Interpreting Linear Regression

 Lesson Six: Other Regression Techniques and Tools for Evaluation

Evaluating the Accuracy of a Regression Model

Using Regularization for Feature Selection

Tree-Based Regression Models

 Lesson Seven: Supervised Learning: Predicting Customer Churn

Classification Problems

Understanding Logistic Regression

Creating a Data Science Pipeline

 Lesson Eight: Fine-Tuning Classification Algorithms

Support Vector Machine

Decision Trees

Random Forest

Preprocessing Data for Machine Learning Models

Model Evaluation

Performance Metrics

 Lesson Nine: Modeling Customer Choice

Understanding Multiclass Classification

Class Imbalanced Data

$216.00 USD

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