Course Summary
This course teaches you how to use predictive modeling and analytics to make data-driven decisions. Learn about various modeling techniques and how to apply them to real-world datasets.Key Learning Points
- Understand the fundamentals of predictive modeling and analytics
- Learn how to evaluate different modeling techniques and select the best one for your data
- Apply predictive modeling and analytics to real-world datasets
Job Positions & Salaries of people who have taken this course might have
- USA: $62,453 - $105,000
- India: ₹355,000 - ₹1,500,000
- Spain: €30,000 - €45,000
- USA: $62,453 - $105,000
- India: ₹355,000 - ₹1,500,000
- Spain: €30,000 - €45,000
- USA: $85,000 - $170,000
- India: ₹600,000 - ₹3,000,000
- Spain: €35,000 - €60,000
- USA: $62,453 - $105,000
- India: ₹355,000 - ₹1,500,000
- Spain: €30,000 - €45,000
- USA: $85,000 - $170,000
- India: ₹600,000 - ₹3,000,000
- Spain: €35,000 - €60,000
- USA: $57,000 - $105,000
- India: ₹350,000 - ₹1,500,000
- Spain: €30,000 - €45,000
Related Topics for further study
Learning Outcomes
- Ability to apply predictive modeling and analytics techniques to real-world datasets
- Understanding of different modeling techniques and how to evaluate them
- Knowledge of best practices for data-driven decision making
Prerequisites or good to have knowledge before taking this course
- Basic understanding of statistics and probability
- Familiarity with programming in Python
Course Difficulty Level
IntermediateCourse Format
- Online self-paced
- Video lectures
- Hands-on coding exercises
Similar Courses
- Applied Data Science with Python
- Data Analytics for Business
- Data Science Essentials
Related Education Paths
Notable People in This Field
- Principal Data Scientist and Astrophysicist
- Data Scientist and Founder of Fast Forward Labs
Related Books
Description
Welcome to the second course in the Data Analytics for Business specialization!
Outline
- Exploratory Data Analysis and Visualizations
- Introduction to the Course
- 0. Introduction to the Module. Why Exploratory Data Analysis is Important
- 1. Data Cleanup and Transformation
- 2. Dealing With Missing Values
- 3. Dealing with Outliers
- 4. Adding and Removing Variables
- 5. Common Graphs
- 6. What is Good Data Visualization?
- Register for Analytic Solver Platform for Education (ASPE)
- Week 1 Quiz
- Week 1 Application Assignment 1 (optional): Data Cleanup
- Predicting a Continuous Variable
- 0. Introduction to Predictive Modeling
- 1. Introduction to Linear Regression
- 2. Assessing Predictive Accuracy Using Cross-Validation
- 3. Multiple Regression
- 4. Improving Model Fit
- 5. Model Selection
- 6. Challenges of Predictive Modeling
- 7. How to Build a Model using XLMiner
- Week 2 Quiz
- Week 2 Application Assignment
- Predicting a Binary Outcome
- 0. Introduction to classification
- 1. Introduction to Logistic Regression
- 2. Building Logistic Regression Model
- 3. Multiple Logistic Regression
- 4. Cross Validation and Confusion Matrix
- 5. Cost Sensitive Classification
- 6. Comparing Models Independent of Costs and Cutoffs
- 7. Building Logistic Regression Models using XLMiner
- Week 3 Quiz
- Week 3 Application Assignment
- Trees and Other Predictive Models
- 0.Introduction to Advanced Predictive Modeling Techniques
- 1. Introduction to Trees
- 2. Classification Trees
- 3. Regression Trees
- 4. Bagging, Boosting, Random Forest
- 5. Building Trees with XLMiner
- 6. Neural Networks
- 7. Building Neural Networks using XLMiner
- Week 4 Quiz
- Week 4 Application Assignment
- Final Course Assignment Quiz
Summary of User Reviews
Find out what users have to say about Coursera's Predictive Modeling and Analytics course. Discover why this course is a popular choice for those interested in predictive modeling and analytics. Overall, users have rated this course highly for its comprehensive coverage of the subject matter.Key Aspect Users Liked About This Course
Many users appreciated the practical approach of the course, allowing them to apply the concepts they learned to real-life scenarios.Pros from User Reviews
- Clear and concise explanations of complex topics
- Excellent examples and case studies
- Interactive assignments and quizzes for hands-on learning
- Comprehensive coverage of predictive modeling and analytics
Cons from User Reviews
- Some users found the course to be too advanced for beginners
- Limited interaction with instructors
- The course may require prior knowledge of statistics and programming
- The workload can be challenging for those with busy schedules
Dan Zhang
3.6 Raiting
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