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
Excellent course with amazing practical exercises!
The entire course is an overview! This course will be a revision if you already know the concepts.
Fantastic course! Excellent conceptual teaching for people who already know the subject but need some more clarity on how to approach statistical tests and machine learning.
Nive that the course covered a broad range of topics.
And good to get pushed to do some kaggle competition and peer review.
About the Data Science at Scale Specialization

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