Course Summary
This course teaches you how to use data mining techniques to make predictions about the future, including how to analyze data sets, build models, and make predictions using those models.Key Learning Points
- Learn how to use data mining techniques to make predictions
- Analyze data sets and build models
- Gain practical experience with real-world data sets
Job Positions & Salaries of people who have taken this course might have
- Data Analyst
- USA: $60,000
- India: ₹4,50,000
- Spain: €30,000
- Business Intelligence Analyst
- USA: $75,000
- India: ₹6,00,000
- Spain: €40,000
- Data Scientist
- USA: $110,000
- India: ₹12,00,000
- Spain: €70,000
Related Topics for further study
Learning Outcomes
- Understand the basics of data mining and predictive analytics
- Analyze and build models using real-world data sets
- Make predictions about future events based on your analysis
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of statistics
- Familiarity with programming languages such as R or Python
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Data Mining and Machine Learning
- Applied Data Science with Python
Related Education Paths
Notable People in This Field
- Principal Data Scientist at Booz Allen Hamilton
- Founder of Fast Forward Labs
Related Books
Description
This course introduces students to the science of business analytics while casting a keen eye toward the artful use of numbers found in the digital space. The goal is to provide businesses and managers with the foundation needed to apply data analytics to real-world challenges they confront daily in their professional lives. Students will learn to identify the ideal analytic tool for their specific needs; understand valid and reliable ways to collect, analyze, and visualize data; and utilize data in decision making for their agencies, organizations or clients.
Outline
- Module 0: Get Ready & Module 1: Drowning in Data, Starving for Knowledge
- Welcome to Predictive Analytics and Data Mining
- Meet Professor Sridhar Seshadri
- Rattle Installation Guidelines for Windows
- R and Rattle Installation Instructions for Mac OS
- Overview of Rattle
- Lecture 1-1: Introduction to Clustering
- Lecture 1-2: Applications of Clustering
- Lecture 1-3: How to Cluster
- Lecture 1-4: Introduction to K Means
- Lecture 1-5: Hierarchical (Agglomerative) Clustering
- Lecture 1-6: Measuring Similarity Between Clusters
- Lecture 1-7: Real World Clustering Example
- Lecture 1-8: Clustering Practice and Summary
- Syllabus
- About the Discussion Forums
- Glossary
- Brand Descriptions
- Update Your Profile
- Module 0 Agenda
- Rattle Tutorials (Interface, Windows, Mac)
- Frequent Asked Questions
- Module 1 Overview
- Module 1 Readings, Data Sets, and Slides
- Module 1 Peer Review Assignment Answer Key
- Orientation Quiz
- Module 1 Practice Problems
- Module 1 Graded Quiz
- Module 2: Decision Trees
- Lecture 2-1: Introduction to Discriminative Classifiers
- Lecture 2-2: Model Complexity
- Lecture 2-3: Rule Based Classifiers
- Lecture 2-4: Entropy and Decision Trees
- Lecture 2-5: Classification Tree Example
- Lecture 2-6: Regression Tree Example
- Lecture 2-7: Introduction to Forests and Spam Filter Exercise
- Module 2 Overview
- Module 2 Readings, Data Sets, and Slides
- Module 2 Peer Review Assignment Answer Key
- Module 2 Practice Problems
- Module 2 Graded Quiz
- Module 3: Rules, Rules, and More Rules
- Lecture 3-1: Introduction to Rules
- Lecture 3-2: K-Nearest Neighbor
- Lecture 3-3: K-Nearest Neighbor Classifier
- Lecture 3-4: Selecting the Best K in Rstudio
- Lecture 3-5: Bayes' Rule
- Lecture 3-6: The Naïve Bayes Trick
- Lecture 3-7: Employee Attrition Example
- Lecture 3-8: Employee Attrition Example in Rstudio, Exercise, and Summary
- Module 3 Overview
- Module 3 Readings, Data Sets, and Slides
- Module 3 Peer Review Assignment Answer Key
- Module 3 Practice Problems
- Module 3 Graded Quiz
- Module 4: Model Performance and Recommendation Systems
- Lecture 4-1: Introduction to Model Performance
- Lecture 4-2: Classification Tree Example
- Lecture 4-3: True and False Negatives
- Lecture 4-4: Clock Example Exercise
- Lecture 4-5: Making Recommendations
- Lecture 4-6: Association Rule Mining
- Lecture 4-7: Collaborative Filtering
- Lecture 4-8: Recommendation Example in Rstudio and Summary
- Module 4 Overview
- Module 4 Readings, Data Sets, and Slides
- Module 4 Peer Review Assignment Answer Key
- Module 4 Practice Problems
- Module 4 Graded Quiz
Summary of User Reviews
Discover the principles and techniques of predictive analytics and data mining with this top-rated course on Coursera. Many users praise the course for its practical examples and real-life applications that make it easy to understand and apply the concepts.Key Aspect Users Liked About This Course
Practical examples and real-life applicationsPros from User Reviews
- Clear and concise explanations
- Great introduction to predictive analytics
- Lots of hands-on exercises and quizzes
- Engaging and knowledgeable instructors
- Flexible schedule and self-paced learning
Cons from User Reviews
- Some users found the course too basic
- Lack of advanced topics and techniques
- Not enough depth on some topics
- Some technical issues with the platform
- Limited interaction with other students
Sridhar Seshadri
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