Brief Introduction
Segmentation is used by companies across industries to better target the right products to the right customers. Most segmentation approaches are rather simple, such as segmenting customers by geography or age. However, with the rich amount of data business have now, much more sophisticated segmentation approaches are available. In this course, you'll learn how to use an advanced analytical method called clustering to create useful segments for business contexts, whether its stores, customers, geCourse Summary
Learn how to segment and cluster data using various techniques and algorithms in this hands-on course. Gain practical skills to apply in real-world scenarios.Key Learning Points
- Understand the difference between segmentation and clustering
- Learn various techniques and algorithms for segmentation and clustering
- Practice using Python and Jupyter Notebook for data analysis
Job Positions & Salaries of people who have taken this course might have
- Data Scientist
- USA: $113,309
- India: ₹1,416,000
- Spain: €34,000
- Machine Learning Engineer
- USA: $112,857
- India: ₹1,385,000
- Spain: €32,000
- Business Intelligence Analyst
- USA: $69,633
- India: ₹720,000
- Spain: €24,000
Related Topics for further study
Learning Outcomes
- Ability to apply segmentation and clustering techniques to real-world data sets
- Proficiency in using Python and Jupyter Notebook for data analysis
- Understanding of the difference between segmentation and clustering and their respective use cases
Prerequisites or good to have knowledge before taking this course
- Proficiency in Python programming
- Basic understanding of machine learning concepts
Course Difficulty Level
IntermediateCourse Format
- Self-paced online course
- Hands-on projects and exercises
- Video lectures and readings
Similar Courses
- Data Mining and Analysis
- Machine Learning Engineer
- Data Scientist
Related Education Paths
Notable People in This Field
- Kirk Borne
- Hilary Mason
- Yann LeCun
Related Books
Description
The Segmentation & Clustering course provides students with the knowledge to build and apply clustering models to develop sophisticated segmentation in business contexts.Requirements
- No programming experience required Interested in using data to make better business decisions Alteryx license (provided to Nanodegree students at no cost, compatible with Windows only) See the Technology Requirements for using Udacity.
Knowledge
- Instructor videosLearn by doing exercisesTaught by industry professionals
Outline
- lesson 1 Segmentation and Clustering Fundamentals Learn the difference between standardization and localization. Learn about the concept of distance in clustering models. Get introduced to how segmentation is used in business. lesson 2 Data Preparation for Clustering Models Learn how to select data for clustering models. Learn what data types can be used in clustering models. Scale and transform data for clustering models. lesson 3 Variable Reduction Learn the difference between factor analysis and principle components analysis. Learn to use principal components analysis to reduce the number of variables in a model. lesson 4 Clustering models design Learn the difference between k-centroid and hierarchical clustering models. Be able to select the number of clusters for a k-centroid model. Validate your clusters in Alteryx. lesson 5 Lesson 5 – Building a Clustering Model Build a k-centroid clustering model to segment retail stores. Learn how to visualize and validate your clusters. Be able to interpret the results and communicate the “story” of the analysis.
Summary of User Reviews
The Segmentation and Clustering course on Udacity has received positive reviews from users. Students have found the course to be comprehensive and well-structured, with clear explanations and practical examples. Many users appreciated the hands-on approach to learning and the real-world applications of the techniques taught in the course.Key Aspect Users Liked About This Course
The practical examples and real-world applications of the techniques taught in the course have been praised by many users.Pros from User Reviews
- Comprehensive and well-structured course material
- Clear explanations and practical examples
- Hands-on approach to learning
- Real-world applications of the techniques taught
- Opportunities to practice and apply the concepts learned
Cons from User Reviews
- Some users found the pace of the course to be too slow
- Limited guidance on advanced topics
- Not suitable for beginners with no background in data analysis
- Some technical issues with the course platform reported
- Some users found the assignments to be too challenging
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