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.
Practical Predictive Analytics: Models and Methods
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Practical Predictive Analytics: Models and Methods
This course is part of Data Science at Scale Specialization
Instructor: Bill Howe
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323 reviews
8 hours to complete
Flexible schedule
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Skills you'll gain
- Statistical Analysis
- Statistical Methods
- Network Analysis
- Statistics
- Model Optimization
- Graph Theory
- Decision Tree Learning
- Statistical Inference
- Machine Learning Methods
- Machine Learning
- Analytics
- Applied Machine Learning
- Supervised Learning
- Data Science
- Data Analysis
- Predictive Analytics
- Big Data
- Unsupervised Learning
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Taught in English
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This course is part of the Data Science at Scale Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course
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Showing 3 of 323
SP
Reviewed on Dec 22, 2016
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.
TR
Reviewed on Feb 16, 2016
Its a great review course. Prior knowledge is necessary
NE
Reviewed on Jun 7, 2017
I think the amount of course work to lectures was more appropriate than the first segment. I enjoyed the exercises and felt that they mixed the correct amount of theory and applicaiton.
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