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Introduction to Customer Segmentation in Python
Ari Anastassiou
Ari Anastassiou
In this 2 hour long project, you will learn how to approach a customer purchase dataset, and how to explore the intricacies of such a dataset. You will learn the basic underlying ideas behind Principal Component Analysis, Kernel Principal Component Analysis, and K-Means Clustering. You will learn how to leverage these concepts, paired with industry knowledge and auxiliary modeling concepts to segment the customers of a certain store, and find similarities and differences between different clusters using unsupervised machine learning techniques.
Read more
In this 2 hour long project, you will learn how to approach a customer purchase dataset, and how to explore the intricacies of such a dataset. You will learn the basic underlying ideas behind Principal Component Analysis, Kernel Principal Component Analysis, and K-Means Clustering. You will learn how to leverage these concepts, paired with industry knowledge and auxiliary modeling concepts to segment the customers of a certain store, and find similarities and differences between different clusters using unsupervised machine learning techniques.
Read more
In this 2 hour long project, you will learn how to approach a customer purchase dataset, and how to explore the intricacies of such a dataset. You will learn the basic underlying ideas behind Principal Component Analysis, Kernel Principal Component Analysis, and K-Means Clustering. You will learn how to leverage these concepts, paired with industry knowledge and auxiliary modeling concepts to segment the customers of a certain store, and find similarities and differences between different clusters using unsupervised machine learning techniques.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/ovxznh/introduction
What's inside
Syllabus
Interactive Machine Learning Dashboards using Plotly Dash
In this 2 hour long project, you will learn how to approach a customer purchase dataset, and how to explore the intricacies of such a dataset. You will learn the basic underlying ideas behind Principal Component Analysis, Kernel Principal Component Analysis, and K-Means Clustering. You will learn how to leverage these concepts, paired with industry knowledge and auxiliary modeling concepts to segment the customers of a certain store, and find similarities and differences between different clusters using unsupervised machine learning techniques.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores the fundamentals of PCA, KPCA, and K-Means Clustering, providing a strong foundation in unsupervised machine learning concepts
Focuses on the practical application of these techniques for customer segmentation, demonstrating their real-world relevance
Utilizes Plotly Dash for interactive machine learning dashboards, enhancing the learning experience with visual representations
Suitable for learners with no prior knowledge in machine learning, offering a clear and accessible introduction to the concepts
Designed for learners based in North America, which may limit accessibility for those in other regions
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Reviews summary
Well-structured segmentation intro
Learners say this course provides a practical introduction to customer segmentation in Python with engaging assignments and a clear, well-organized structure.
Well-organized, clear structure.
"Structure of course was easy to follow."
"The whole project was well organized"
Practical, hands-on training.
"very practical"
"good hands-on training"
Dataset could be improved.
"The actual dataset and the results of the analysis were not impactful or very interesting"
Activities
Coming soon
We're preparing activities for
Introduction to Customer Segmentation in Python.
These are activities you can do either before, during, or after a course.
Career center
Learners who complete Introduction to Customer Segmentation in Python will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, and deploy machine learning models. This course can help you learn the fundamentals of machine learning, including unsupervised learning techniques such as clustering, which are essential for this role.
Statistician
Statistician
Statisticians use data to design and conduct studies, and to analyze and interpret data. This course can help you develop the skills needed to succeed in this role, such as data analysis, statistical modeling, and experimental design.
Data Scientist
Data Scientist
Data Scientists use their knowledge of statistics, programming, and machine learning to extract insights from data. This course can help you develop the skills needed to succeed in this role, such as data exploration, dimensionality reduction, and unsupervised learning.
Marketing Analyst
Marketing Analyst
Marketing Analysts use data to help businesses understand their customers and market their products and services more effectively. This course can help you develop the skills needed to succeed in this role, such as customer segmentation, market research, and campaign analysis.
Data Analyst
Data Analyst
As a Data Analyst, you would be responsible for collecting, cleaning, and analyzing data to identify trends and patterns. This course can help you build a foundation in data analysis techniques, such as Principal Component Analysis and K-Means Clustering, which can be invaluable in this role.
Quant Analyst
Quant Analyst
Quant Analysts use data to develop and implement quantitative models for investment decisions. This course can help you develop the skills needed to succeed in this role, such as data analysis, financial modeling, and programming.
Actuary
Actuary
Actuaries use data to assess and manage risks for insurance companies and other financial institutions. This course can help you develop the skills needed to succeed in this role, such as data analysis, financial modeling, and risk management.
Product Manager
Product Manager
Product Managers are responsible for the development and management of products. This course can help you develop the skills needed to succeed in this role, such as customer segmentation, market research, and product development.
Data Architect
Data Architect
Data Architects design and build the infrastructure that supports data storage, processing, and analysis. This course can help you develop the skills needed to succeed in this role, such as data modeling, data integration, and data security.
Data Engineer
Data Engineer
Data Engineers build and maintain the infrastructure that supports data analysis and machine learning. This course can help you develop the skills needed to succeed in this role, such as data preprocessing, data integration, and data governance.
Financial Analyst
Financial Analyst
Financial Analysts use data to help businesses make financial decisions. This course can help you develop the skills needed to succeed in this role, such as data analysis, financial modeling, and valuation.
Data Privacy Analyst
Data Privacy Analyst
Data Privacy Analysts are responsible for protecting the privacy of data. This course can help you develop the skills needed to succeed in this role, such as data security, data protection, and compliance.
Business Analyst
Business Analyst
Business Analysts use data to help businesses make better decisions. This course can help you develop the skills needed to succeed in this role, such as data analysis, data visualization, and communication.
Risk Analyst
Risk Analyst
Risk Analysts use data to help businesses identify and manage risks. This course can help you develop the skills needed to succeed in this role, such as data analysis, risk modeling, and portfolio management.
Data Governance Manager
Data Governance Manager
Data Governance Managers are responsible for developing and implementing policies and procedures for data management. This course can help you develop the skills needed to succeed in this role, such as data quality management, data security, and compliance.
For more career information including salaries, visit:
OpenCourser.com/course/ovxznh/introduction
Reading list
We've selected 14 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
Introduction to Customer Segmentation in Python.
This comprehensive book provides a theoretical and practical treatment of pattern recognition and machine learning. It covers a wide range of topics, including supervised and unsupervised learning, neural networks, and Bayesian methods. It serves as an excellent reference for those interested in a deep understanding of machine learning.
This comprehensive book covers machine learning techniques, including supervised and unsupervised learning, model evaluation, and deep learning. It provides hands-on examples in Python using popular libraries like Scikit-Learn, Keras, and TensorFlow. It serves as an excellent reference for this course.
This comprehensive book provides a solid theoretical foundation in statistical learning. It covers supervised and unsupervised learning, model selection, and regularization techniques. It serves as an excellent reference for those interested in understanding the mathematical foundations of machine learning.
Offers a probabilistic perspective on machine learning. It covers Bayesian inference, graphical models, and reinforcement learning. It provides a deep understanding of the underlying principles of machine learning and is suitable for those with a strong mathematical background.
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Offers a comprehensive overview of predictive modeling techniques, including supervised and unsupervised learning, model selection, and evaluation. It provides a solid foundation for this course and serves as a valuable reference for those interested in building predictive models.
This practical book provides a comprehensive overview of machine learning with Python. It covers various techniques, including feature engineering, model selection, and evaluation. It offers a solid foundation for this course and serves as a valuable reference.
Offers a practical approach to machine learning with Python, covering various techniques such as supervised and unsupervised learning, model evaluation, and feature engineering. It provides hands-on examples and exercises, making it a useful reference for this course.
While this book focuses on deep learning, it provides valuable insights into machine learning concepts and techniques. It covers deep learning models, training, and evaluation, and serves as an excellent resource for those interested in exploring deep learning applications.
Offers a hands-on approach to machine learning with Python. It covers various techniques, including classification, regression, and clustering, and provides practical examples and exercises. It serves as a valuable resource for this course, especially for those interested in gaining practical experience.
Save
Covers data mining fundamentals, techniques, and applications using Python. It provides a comprehensive understanding of data preprocessing, feature selection, classification, clustering, and more. It serves as a valuable reference for this course, especially for those interested in exploring data mining concepts in depth.
Provides a concise yet comprehensive overview of machine learning concepts. It covers various techniques, including supervised and unsupervised learning, model evaluation, and applications. It serves as a valuable resource for this course, especially for those interested in gaining a broad understanding of machine learning.
Provides a practical guide to data science for business professionals. It covers data collection, analysis, and visualization, as well as machine learning applications. It offers valuable insights for those interested in understanding the role of data science in business decision-making.
Save
Provides a business-oriented perspective on machine learning applications. It explores the use of machine learning in various industries, providing valuable insights for those interested in understanding the practical implications of machine learning.
Provides a gentle introduction to machine learning with Python, making it accessible to learners with no prior knowledge. It covers essential concepts, algorithms, and hands-on examples, serving as a great starting point for this course.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ovxznh/introduction
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Effort
2 hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Ari Anastassiou
Language
English
Good to know
Explores the fundamentals of PCA, KPCA, and K-Means Clustering, providing a strong foundation in unsupervised machine learning concepts
Focuses on the practical application of these techniques for customer segmentation, demonstrating their real-world relevance
Utilizes Plotly Dash for interactive machine learning dashboards, enhancing the learning experience with visual representations
Suitable for learners with no prior knowledge in machine learning, offering a clear and accessible introduction to the concepts
Designed for learners based in North America, which may limit accessibility for those in other regions
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