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Machine Learning with PySpark
Customer Churn Analysis
Ahmad Varasteh
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/97rkse/machine
What's inside
Syllabus
Project Overview
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.
Good to know
Good to know
Know what's good ,
what to watch for , 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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Activities
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Machine Learning with PySpark: Customer Churn Analysis.
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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 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.
Machine Learning Engineer
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.
Data Analyst
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.
Business 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.
Customer Success Manager
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.
Data Engineer
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.
Software 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.
Statistician
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.
Marketing Analyst
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.
Product Manager
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.
Financial Analyst
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.
Operations Research 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.
Risk 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.
Consultant
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.
Technical Writer
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.
For more career information including salaries, visit:
OpenCourser.com/course/97rkse/machine
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/97rkse/machine
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Ahmad Varasteh
Language
English
Good to know
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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