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Machine Learning with PySpark

Customer Churn Analysis

Ahmad Varasteh

This 90-minute guided-project, "Pyspark for Data Science: Customer Churn Prediction," is a comprehensive guided-project that teaches you how to use PySpark to build a machine learning model for predicting customer churn in a Telecommunications company. This guided-project covers a range of essential tasks, including data loading, exploratory data analysis, data preprocessing, feature preparation, model training, evaluation, and deployment, all using Pyspark. We are going to use our machine learning model to identify the factors that contribute to customer churn, providing actionable insights to the company to reduce churn and increase customer retention. Throughout the guided-project, you'll gain hands-on experience with different steps required to create a machine learning model in Pyspark, giving you the tools to deliver an AI-driven solution for customer churn. Prerequisites for this guided-project include basic knowledge of Machine Learning and Decision Trees, as well as familiarity with Python programming concepts such as loops, if statements, and lists.

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What's inside

Syllabus

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Course is intended for learners who understand the basic concepts of machine learning, as well as how decision trees are utilized for predictions
Taught by Ahmad Varasteh, who is well-known for their work in machine learning and data science
Teaches PySpark, a widely-used tool in the industry for handling big data
Teaches learners how to use PySpark to build a machine learning model to reduce customer churn in a telecommunications company
Builds a strong foundation for learners who want to apply PySpark to real-world data science problems
Offers a comprehensive and hands-on experience in the field of data science

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Reviews summary

Practical pyspark churn analysis guide

According to students, this 90-minute guided project offers a highly practical and hands-on introduction to applying PySpark for customer churn analysis. Learners appreciate its focused approach, which helps solidify understanding of the machine learning pipeline from data loading to model deployment within a real-world telecommunications context. While the course provides a clear pathway to an AI-driven solution, some note that a solid grasp of Python and basic machine learning concepts is essential to fully benefit from this quick, application-oriented experience.
Requires solid basic ML and Python concepts to follow smoothly.
"Make sure you have a good grasp of Python basics and ML concepts before starting, as it moves at a quick pace."
"If you're new to machine learning or PySpark, this might be too fast; it assumes you know the fundamentals."
"I already had some ML background, so this course was perfect for jumping straight into a PySpark application without much theoretical overhead."
A good entry point to using PySpark for machine learning tasks.
"I found it to be a very helpful introduction to using PySpark for real-world machine learning problems."
"The course clearly demonstrates the PySpark MLlib functions for feature engineering and model building."
"It gave me confidence to start exploring and using PySpark in my own data science projects."
A short, targeted project ideal for quick skill application.
"At just 90 minutes, it's perfect for solidifying concepts without a huge time commitment."
"Don't expect a deep dive; it's a guided project to get practical experience, and it excels at that specific goal."
"This course is designed to be a quick application, not a comprehensive theory class, which I truly appreciated for its efficiency."
Excellent for hands-on experience in a real-world scenario.
"I found the practical application of PySpark to customer churn analysis very useful for my work."
"This guided project provided clear steps to build a machine learning model, which was great for a quick hands-on experience."
"It's great for gaining practical skills and understanding how to deliver an AI-driven solution quickly for business problems."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Machine Learning with PySpark: Customer Churn Analysis with these activities:
PySpark Beginner Tutorial
Start with this tutorial to understand foundational PySpark concepts before delving into the course.
Browse courses on Pyspark
Show steps
  • Read beginner-friendly documentation
  • Complete hands-on exercises provided in the guide
Connect with PySpark Professionals
Attend conferences and online forums to learn from and network with experienced PySpark professionals.
Browse courses on Networking
Show steps
  • Join online PySpark forums
  • Attend PySpark conferences
PySpark Programming Exercises
These exercises will help you practice writing PySpark code and solidify your understanding of core concepts.
Browse courses on Pyspark
Show steps
  • Solve coding problems
Three other activities
Expand to see all activities and additional details
Show all six activities
Machine Learning Algorithms Practice
Practice implementing common machine learning algorithms like Decision Trees.
Browse courses on Machine Learning Algorithms
Show steps
  • Implement algorithms in PySpark
Customer Churn Prediction Model
Build a machine learning model for customer churn prediction using PySpark.
Browse courses on Customer Churn Prediction
Show steps
  • Gather and prepare data
  • Train and evaluate machine learning model
  • Deploy machine learning model
PySpark Blog Post
Write a blog post on PySpark to reinforce your knowledge and share your insights with others.
Browse courses on Pyspark
Show steps
  • Choose a topic to write about
  • Research and gather information
  • Write and publish your blog post

Career center

Learners who complete Machine Learning with PySpark: Customer Churn Analysis will develop knowledge and skills that may be useful to these careers:
Data Scientist
Data Scientists play a key role in the Telecommunications industry, where they apply machine learning techniques to analyze customer data and identify patterns that contribute to churn. This course, 'Machine Learning with PySpark: Customer Churn Analysis,' provides a solid foundation in using PySpark for data loading, exploration, preprocessing, feature preparation, model training, and evaluation, giving you the skills necessary to succeed as a Data Scientist in this field.
See salaries and explore the career path for Data Scientist
Machine Learning Engineer
Machine Learning Engineers are responsible for building and deploying machine learning models in the Telecommunications industry. This course provides a comprehensive overview of the machine learning lifecycle using PySpark, including data loading, preprocessing, feature engineering, model selection, training, and evaluation. These skills are essential for success as a Machine Learning Engineer working on customer churn prediction.
See salaries and explore the career path for Machine Learning Engineer
Data Analyst
Data Analysts in the Telecommunications industry use data analysis techniques to understand customer behavior and identify trends that impact churn. This course provides a solid foundation in data loading, exploration, and visualization using PySpark, which are crucial skills for Data Analysts working on customer churn analysis.
See salaries and explore the career path for Data Analyst
Business Analyst
Business Analysts in the Telecommunications industry use data analysis and machine learning techniques to understand customer needs and develop strategies to reduce churn. This course provides a comprehensive overview of customer churn analysis using PySpark, including data loading, exploration, feature preparation, model training, and evaluation, giving you the skills to excel as a Business Analyst in this field.
See salaries and explore the career path for Business Analyst
Customer Success Manager
Customer Success Managers in the Telecommunications industry are responsible for building and maintaining relationships with customers to prevent churn. This course provides insights into customer churn analysis using PySpark, enabling you to identify factors contributing to churn and develop strategies to improve customer satisfaction and retention.
See salaries and explore the career path for Customer Success Manager
Data Engineer
Data Engineers in the Telecommunications industry are responsible for building and maintaining the data infrastructure that supports machine learning models for customer churn prediction. This course provides a solid foundation in data loading, transformation, and storage using PySpark, giving you the skills necessary to succeed as a Data Engineer in this field.
See salaries and explore the career path for Data Engineer
Software Engineer
Software Engineers in the Telecommunications industry may work on developing or maintaining software applications that use machine learning for customer churn prediction. This course provides a comprehensive overview of using PySpark for data analysis and machine learning, giving you the technical skills to contribute to these projects.
See salaries and explore the career path for Software Engineer
Statistician
Statisticians in the Telecommunications industry use statistical techniques to analyze customer data and identify patterns that contribute to churn. This course provides a solid foundation in data exploration, statistical modeling, and hypothesis testing using PySpark, which are essential skills for Statisticians working on customer churn analysis.
See salaries and explore the career path for Statistician
Marketing Analyst
Marketing Analysts in the Telecommunications industry use data analysis techniques to understand customer behavior and develop marketing campaigns to reduce churn. This course provides a comprehensive overview of customer churn analysis using PySpark, including data loading, exploration, feature preparation, and model training, giving you the skills to succeed as a Marketing Analyst in this field.
See salaries and explore the career path for Marketing Analyst
Product Manager
Product Managers in the Telecommunications industry are responsible for developing and managing products and services that meet customer needs and reduce churn. This course provides insights into customer churn analysis using PySpark, enabling you to identify factors contributing to churn and develop strategies to improve product offerings and customer satisfaction.
See salaries and explore the career path for Product Manager
Financial Analyst
Financial Analysts in the Telecommunications industry may be involved in analyzing financial data to understand the impact of customer churn on revenue and profitability. This course provides a solid foundation in data loading, exploration, and visualization using PySpark, which are valuable skills for Financial Analysts working on customer churn analysis.
See salaries and explore the career path for Financial Analyst
Operations Research Analyst
Operations Research Analysts in the Telecommunications industry use mathematical and statistical techniques to optimize operations and reduce costs, including customer churn. This course provides a comprehensive overview of using PySpark for data analysis and machine learning, giving you the technical skills to contribute to these projects.
See salaries and explore the career path for Operations Research Analyst
Risk Analyst
Risk Analysts in the Telecommunications industry assess and manage risks associated with customer churn. This course provides insights into customer churn analysis using PySpark, enabling you to identify factors contributing to churn and develop strategies to mitigate risks and improve customer retention.
See salaries and explore the career path for Risk Analyst
Consultant
Consultants in the Telecommunications industry may provide expertise in customer churn analysis and develop strategies to reduce churn for their clients. This course provides a comprehensive overview of using PySpark for data analysis and machine learning, giving you the skills to contribute to these projects.
See salaries and explore the career path for Consultant
Technical Writer
Technical Writers in the Telecommunications industry may be involved in documenting customer churn analysis processes and findings. This course provides a solid foundation in data exploration, statistical modeling, and hypothesis testing using PySpark, which are valuable skills for Technical Writers working on customer churn analysis.
See salaries and explore the career path for Technical Writer

Reading list

We've selected 11 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Machine Learning with PySpark: Customer Churn Analysis.
Cover image
Python for Data Analysis
Save
Provides a comprehensive introduction to Python for data analysis, covering topics such as data manipulation, visualization, and machine learning. It valuable resource for anyone looking to use Python for data-related tasks.
Python for Data Analysis
Paperback
Check
Python for Data Analysis
Kindle Edition
Check
Cover image
Probabilistic Machine Learning
Save
Provides a comprehensive overview of machine learning from a probabilistic perspective, covering topics such as supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for anyone looking to learn about the latest advances in machine learning.
Probabilistic Machine Learning: Advanced Topics...
Hardcover
$$$$
Cover image
Big Data
Save
Provides a comprehensive overview of big data and scalable real-time data systems, covering topics such as data storage, processing, and analysis. It valuable resource for anyone looking to learn about the latest advances in big data.
Big Data: Principles and best practices of scalable...
Paperback
$$
(Deutsch) Big Data: Entwicklung und Programmierung von...
Perfect Paperback
$$$
Big Data: Principles and best practices of scalable...
Kindle Edition
$$
Cover image
Machine Learning in Action
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Provides a hands-on guide to machine learning, covering topics such as data preprocessing, feature engineering, and model training. It valuable resource for anyone looking to learn about the latest advances in machine learning.
Machine Learning in Action
Paperback
$$
(中文) 机器学习实战 (图灵程序设计丛书 72) (Chinese Edition)
Kindle Edition
$
Cover image
Reinforcement Learning, second edition
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Provides a comprehensive overview of reinforcement learning, covering topics such as Markov decision processes, value functions, and policy optimization. It valuable resource for anyone looking to learn about the latest advances in reinforcement learning.
Reinforcement Learning, second edition: An...
Hardcover
$$$
Reinforcement Learning, second edition: An...
Kindle Edition
$$$
Pattern Recognition and Machine Learning
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Classic textbook on machine learning and pattern recognition. It provides a comprehensive overview of the theory and algorithms of machine learning, with a focus on statistical methods. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning, and provides a solid foundation for understanding the fundamental principles of machine learning.
Pattern Recognition and Machine Learning...
Hardcover
$$$
Pattern Recognition and Machine Learning...
Paperback
$$$
Pattern Recognition and Machine Learning (text...
Unknown Binding
$$$$
Cover image
An Introduction to Statistical Learning
Save
Provides a comprehensive overview of statistical learning, covering topics such as supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for anyone looking to learn about the latest advances in statistical learning.
An Introduction to Statistical Learning
Paperback
Check
An Introduction to Statistical Learning
Kindle Edition
Check
Cover image
Deep Learning
Save
Provides a comprehensive overview of deep learning, covering topics such as neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for anyone looking to learn about the latest advances in deep learning.
Deep Learning
Paperback
$$$
Deep Learning (Adaptive Computation and Machine...
Hardcover
$$$
Deep Learning (Adaptive Computation and Machine...
Kindle Edition
$$$
Data Mining
Save
Provides a comprehensive overview of data mining, covering topics such as data preprocessing, feature engineering, and model training. It valuable resource for anyone looking to learn about the latest advances in data mining.
Data Mining: Concepts and Techniques (The Morgan...
Kindle Edition
$$$
Data Mining: Concepts and Techniques (The Morgan...
Hardcover
$$$
Data Mining: Concepts and Techniques, Second...
Hardcover
$$
Data Mining: Know It All
Hardcover
$$$
Cover image
Data Science for Business
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Provides a practical guide to using data science for business, covering topics such as data collection, analysis, and visualization. It valuable resource for anyone looking to learn about the latest advances in data science.
Data Science for Business: What You Need to Know...
Paperback
$$
Data Science for Business: What You Need to Know...
Kindle Edition
$$
Cover image
The Elements of Statistical Learning
Save
Provides a comprehensive overview of statistical learning, covering topics such as supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for anyone looking to learn about the latest advances in statistical learning.
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Kindle Edition
$$$

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Course image
Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Ahmad Varasteh
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Course is intended for learners who understand the basic concepts of machine learning, as well as how decision trees are utilized for predictions
Taught by Ahmad Varasteh, who is well-known for their work in machine learning and data science
Teaches PySpark, a widely-used tool in the industry for handling big data
Teaches learners how to use PySpark to build a machine learning model to reduce customer churn in a telecommunications company
Builds a strong foundation for learners who want to apply PySpark to real-world data science problems
Offers a comprehensive and hands-on experience in the field of data science
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