Data Science for Marketing Analytics

Data Science for Marketing Analytics, available at $44.99, has an average rating of 4.35, with 45 lectures, 9 quizzes, based on 143 reviews, and has 950 subscribers.

You will learn about 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 maximum information This course is ideal for individuals who are 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 is particularly useful for Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts.

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Summary

Title: Data Science for Marketing Analytics

Price: $44.99

Average Rating: 4.35

Number of Lectures: 45

Number of Quizzes: 9

Number of Published Lectures: 45

Number of Published Quizzes: 9

Number of Curriculum Items: 54

Number of Published Curriculum Objects: 54

Original Price: $199.99

Quality Status: approved

Status: Live

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 maximum information

Who Should Attend

Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts.

Target Audiences

Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts.

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.

About the Author

Tommy Blanchard earned his Ph.D. from the University of Rochester and did his postdoctoral training at Harvard. Now, he leads the data science team at Fresenius Medical Care North America. His team performs advanced analytics and creates predictive models to solve a wide variety of problems across the company.

Debasish Behera works as a Data Scientist for a large Japanese corporate bank, where he applies machine learning/AI for solving complex problems. He has worked on multiple use cases involving AML, predictive analytics, customer segmentation, chat bots, and natural language processing. He currently lives in Singapore and holds a Master’s in Business Analytics (MITB) from Singapore Management University.

Pranshu Bhatnagar works as a Data Scientist in the telematics, insurance and mobile software space. He has previously worked as a Quantitative Analyst in the FinTech industry and often writes about algorithms, time series analysis in Python, and similar topics. He graduated with honours from the Chennai Mathematical Institute with a degree in Mathematics and Computer Science and has done certification courses in Machine Learning and Artificial Intelligence from the International Institute of Information Technology, Hyderabad. He is based out of Bangalore, India.

Candas Bilgin is an experienced Data Science Specialist with a demonstrated history of working in the hospital & health care industry. Skilled in Python, R, Machine Learning, Predictive Analytics, and Data Science. Strong engineering professional with a Master of Science (M.Sc.) focused in Electrical, Electronics and Communications Engineering from Yildiz Technical University. He is a Microsoft Certified Data Scientist and also a Certified Tableau Developer.

Course Curriculum

Chapter 1: Data Preparation and Cleaning

Lecture 1: Course Overview

Lecture 2: Lesson Overview

Lecture 3: Data Models and Structured Data

Lecture 4: Pandas

Lecture 5: Data Manipulation

Lecture 6: Summary

Chapter 2: Data Exploration and Visualization

Lecture 1: Lesson Overview

Lecture 2: Identifying the Right Attributes

Lecture 3: Generating Targeted Insights

Lecture 4: Visualizing Data

Lecture 5: Summary

Chapter 3: Unsupervised Learning: Customer Segmentation

Lecture 1: Lesson Overview

Lecture 2: Customer Segmentation Methods

Lecture 3: Similarity and Data Standardization

Lecture 4: k-means Clustering

Lecture 5: Summary

Chapter 4: Choosing the Best Segmentation Approach

Lecture 1: Lesson Overview

Lecture 2: Choosing the Number of Clusters

Lecture 3: Different Methods of Clustering

Lecture 4: Evaluation Clustering

Lecture 5: Summary

Chapter 5: Predicting Customer Revenue Using Linear Regression

Lecture 1: Lesson Overview

Lecture 2: Feature Engineering for Regression

Lecture 3: Performing and Interpreting Linear Regression

Lecture 4: Summary

Chapter 6: Other Regression Techniques and Tools for Evaluation

Lecture 1: Lesson Overview

Lecture 2: Evaluating the Accuracy of a Regression Model

Lecture 3: Using Regularization for Feature Selection

Lecture 4: Tree Based Regression Models

Lecture 5: Summary

Chapter 7: Supervised Learning – Predicting Customer Churn

Lecture 1: Lesson Overview

Lecture 2: Understanding Logistic Regression

Lecture 3: Creating a Data Science Pipeline

Lecture 4: Modeling the Data

Lecture 5: Summary

Chapter 8: Fine-Tuning Classification Algorithms

Lecture 1: Lesson Overview

Lecture 2: Support Vector Machines

Lecture 3: Decision Trees and Random Forests

Lecture 4: Pre-processing Data and Model Evaluation

Lecture 5: Performance Metrics

Lecture 6: Summary

Chapter 9: Modeling Customer Choice

Lecture 1: Lesson Overview

Lecture 2: Understanding Multiclass Classification

Lecture 3: Class Imbalanced Data

Lecture 4: Summary

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 4 votes

2 stars: 14 votes

3 stars: 20 votes

4 stars: 47 votes

5 stars: 58 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

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