Practical Predictive Analytics: Models and Methods
Master statistical experiment design and machine learning methods for effective predictive analytics and data science.
Master statistical experiment design and machine learning methods for effective predictive analytics and data science.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Data Science at Scale Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.1
(317 ratings)
37,585 already enrolled
Instructors:
English
پښتو, বাংলা, اردو, 2 more
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What you'll learn
Design and analyze statistical experiments effectively
Apply resampling methods for robust statistical analysis
Implement classification methods of varying complexity
Master supervised and unsupervised learning techniques
Understand optimization methods including gradient descent
Skills you'll gain
This course includes:
4.83 Hours PreRecorded video
1 quiz
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course focuses on practical applications of statistical experiment design and analytics in data science. Students learn to design effective experiments, analyze results using modern methods, and apply machine learning techniques to real-world problems. The curriculum covers statistical inference, supervised and unsupervised learning, and optimization methods. Through hands-on exercises in R programming, learners develop skills in implementing predictive analytics solutions and understanding common pitfalls in statistical arguments.
Practical Statistical Inference
Module 1 · 2 Hours to complete
Supervised Learning
Module 2 · 2 Hours to complete
Optimization
Module 3 · 41 Minutes to complete
Unsupervised Learning
Module 4 · 1 Hours to complete
Fee Structure
Instructor
Director of Research
Bill Howe is the Director of Research for Scalable Data Analytics at the University of Washington's eScience Institute and holds an Affiliate Assistant Professor position in Computer Science & Engineering. He leads research focused on data management, analytics, and visualization systems tailored for scientific applications. Howe has received multiple awards from Microsoft Research and honors for his contributions to scientific data management.
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4.1 course rating
317 ratings
Frequently asked questions
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