About this course: 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. Learning Goals: After completing this course, you will be able to: 1. Design effective experiments and analyze the results 2. Use resampling methods to make clear and bulletproof statistical arguments without invoking esoteric notation 3. Explain and apply a core set of classification methods of increasing complexity (rules, trees, random forests), and associated optimization methods (gradient descent and variants) 4. Explain and apply a set of unsupervised learning concepts and methods 5. Describe the common idioms of large-scale graph analytics, including structural query, traversals and recursive queries, PageRank, and community detection
Taught by: Bill Howe, Director of Research
Scalable Data Analytics
| Basic Info |
Course 2 of 4 in the
Data Science at Scale Specialization.
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| Commitment | 4 weeks of study, 6-8 hours/week |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
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| Audit | Purchase Course | |
|---|---|---|
| Access to course materials | Available | Available |
| Access to graded materials | Not available | Available |
| Receive a final grade | Not available | Available |
| Earn a shareable Course Certificate | Not available | Available |
This course covers a lot of material, but unfortunately lacks depth and thorough examples in many areas. It could also use more hands-on activities. Overall, I learned quite a bit and found it was worth the time and effort.
The entire course is an overview! This course will be a revision if you already know the concepts.
Nive that the course covered a broad range of topics.
And good to get pushed to do some kaggle competition and peer review.
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SP
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.