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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Data Science at Scale SpecializationUniversity of WashingtonAbout this Course
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Course 2 of 4 in the
Approx. 7 hours to complete
English
Subtitles: French, Portuguese (European), Korean, Russian, English, Spanish
Skills you will gain
- Random Forest
- Predictive Analytics
- Machine Learning
- R Programming
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 2 of 4 in the
Approx. 7 hours to complete
English
Subtitles: French, Portuguese (European), Korean, Russian, English, Spanish
Offered by
University of Washington
Founded in 1861, the University of Washington is one of the oldest state-supported institutions of higher education on the West Coast and is one of the preeminent research universities in the world.
Syllabus - What you will learn from this course
2 hours to complete
Practical Statistical Inference
Learn the basics of statistical inference, comparing classical methods with resampling methods that allow you to use a simple program to make a rigorous statistical argument. Motivate your study with current topics at the foundations of science: publication bias and reproducibility.
2 hours to complete
28 videos (Total 121 min)
3 hours to complete
Supervised Learning
Follow a tour through the important methods, algorithms, and techniques in machine learning. You will learn how these methods build upon each other and can be combined into practical algorithms that perform well on a variety of tasks. Learn how to evaluate machine learning methods and the pitfalls to avoid.
3 hours to complete
26 videos (Total 111 min), 1 reading, 1 quiz
1 hour to complete
Optimization
You will learn how to optimize a cost function using gradient descent, including popular variants that use randomization and parallelization to improve performance. You will gain an intuition for popular methods used in practice and see how similar they are fundamentally.
1 hour to complete
11 videos (Total 41 min)
1 hour to complete
Unsupervised Learning
A brief tour of selected unsupervised learning methods and an opportunity to apply techniques in practice on a real world problem.
1 hour to complete
4 videos (Total 21 min)
Reviews
TOP REVIEWS FROM PRACTICAL PREDICTIVE ANALYTICS: MODELS AND METHODS
by NEJun 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.
by SPDec 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.
by WKJun 5, 2017
Excellent Lectures. Since the course is several years old the organization of some of the assignments needs updating. That's the only reason I gave it 4 instead of 5 stars.
by KPFeb 7, 2016
I enjoy this course. The delivery and the course topics were very interesting. I learnt a lot and peer reviewing other people assignments is a great learning opportunity .
About the Data Science at Scale Specialization
Learn scalable data management, evaluate big data technologies, and design effective visualizations.
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