Statistical experiment design and analytics are at the heart of data science. In this course you will design statistical experiments and analyze the results using modern methods. You will also explore the common pitfalls in interpreting statistical arguments, especially those associated with big data. Collectively, this course will help you internalize a core set of practical and effective machine learning methods and concepts, and apply them to solve some real world problems.
This course is part of the Data Science at Scale Specialization
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About this Course
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Try Coursera for BusinessSkills you will gain
- Random Forest
- Predictive Analytics
- Machine Learning
- R Programming
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Syllabus - What you will learn from this course
Practical Statistical Inference
Supervised Learning
Optimization
Unsupervised Learning
Reviews
- 5 stars48.22%
- 4 stars32.03%
- 3 stars10.03%
- 2 stars5.50%
- 1 star4.20%
TOP REVIEWS FROM PRACTICAL PREDICTIVE ANALYTICS: MODELS AND METHODS
Hands on practices are very good. learning predictive model was a challenge.
Need some background in R or Python and the lectures are from around 2013. Most of the material is still relevant.
Too little people participated and long peer review time.
But the course content is good.
Professor Bill Howe gives great reactions to when there are typos on the slides!
About the Data Science at Scale Specialization

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