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
Practical Predictive Analytics: Models and Methods
This course is part of Data Science at Scale Specialization
Instructor: Bill Howe
37,853 already enrolled
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Gain insight into a topic and learn the fundamentals.
4.1
(320 reviews)
6 hours to complete
3 weeks at 2 hours a week
Flexible schedule
Learn at your own pace
Skills you'll gain
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Shareable certificate
Add to your LinkedIn profile
Assessments
1 assignment
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
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.
What's included
28 videos
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.
What's included
26 videos1 reading1 assignment
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.
What's included
11 videos
A brief tour of selected unsupervised learning methods and an opportunity to apply techniques in practice on a real world problem.
What's included
4 videos1 peer review
Instructor
Offered by
Recommended if you're interested in Data Analysis
Coursera Project Network
University of Illinois Urbana-Champaign
Tableau Learning Partner
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4.1
320 reviews
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9.68%
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5.31%
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Showing 3 of 320
KP
5
Reviewed on Feb 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 .
RS
4
Reviewed on Jun 12, 2017
Very good approach to each method; the assignments are a good test for the topics.
NE
4
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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Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
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When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
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