This course is part of the Data Science at Scale Specialization

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Bill Howe

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Data Science at Scale SpecializationUniversity of Washington

About this Course

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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:
Design effective experiments and analyze the results
Use resampling methods to make clear and bulletproof statistical arguments without invoking esoteric notation
Explain and apply a core set of classification methods of increasing complexity (rules, trees, random forests), and associated optimization methods (gradient descent and variants)
Explain and apply a set of unsupervised learning concepts and methods
Describe the common idioms of large-scale graph analytics, including structural query, traversals and recursive queries, PageRank, and community detection

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

Data Science at Scale Specialization

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

Data Science at Scale Specialization

Approx. 7 hours to complete

English

Subtitles: French, Portuguese (European), Korean, Russian, English, Spanish

Instructor

Bill Howe

Director of Research

Scalable Data Analytics

83,859 Learners

4 Courses

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

Week

Week 1

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)

28 videos

Appetite Whetting: Bad Science5m

Hypothesis Testing5m

Significance Tests and P-Values3m

Example: Difference of Means4m

Deriving the Sampling Distribution6m

Shuffle Test for Significance4m

Comparing Classical and Resampling Methods3m

Bootstrap6m

Resampling Caveats6m

Outliers and Rank Transformation3m

Example: Chi-Squared Test3m

Bad Science Revisited: Publication Bias4m

Effect Size4m

Meta-analysis5m

Fraud and Benford's Law4m

Intuition for Benford's Law2m

Benford's Law Explained Visually3m

Multiple Hypothesis Testing: Bonferroni and Sidak Corrections3m

Multiple Hypothesis Testing: False Discovery Rate4m

Multiple Hypothesis Testing: Benjamini-Hochberg Procedure3m

Big Data and Spurious Correlations4m

Spurious Correlations: Stock Price Example3m

How is Big Data Different?3m

Bayesian vs. Frequentist4m

Motivation for Bayesian Approaches3m

Bayes' Theorem2m

Applying Bayes' Theorem4m

Naive Bayes: Spam Filtering4m

Week

Week 2

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

26 videos

Statistics vs. Machine Learning4m

Simple Examples3m

Structure of a Machine Learning Problem5m

Classification with Simple Rules5m

Learning Rules4m

Rules: Sequential Covering3m

Rules Recap2m

From Rules to Trees2m

Entropy4m

Measuring Entropy4m

Using Information Gain to Build Trees6m

Building Trees: ID3 Algorithm2m

Building Trees: C.45 Algorithm4m

Rules and Trees Recap3m

Overfitting7m

Evaluation: Leave One Out Cross Validation5m

Evaluation: Accuracy and ROC Curves5m

Bootstrap Revisited4m

Ensembles, Bagging, Boosting4m

Boosting Walkthrough5m

Random Forests3m

Random Forests: Variable Importance5m

Summary: Trees and Forests2m

Nearest Neighbor4m

Nearest Neighbor: Similarity Functions4m

Nearest Neighbor: Curse of Dimensionality3m

1 reading

R Assignment: Classification of Ocean Microbes10m

1 practice exercise

R Assignment: Classification of Ocean Microbes30m

Week

Week 3

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)

Week

Week 4

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

4.1

59 reviews

5 stars
48.22%
4 stars
32.03%
3 stars
10.03%
2 stars
5.50%
1 star
4.20%

TOP REVIEWS FROM PRACTICAL PREDICTIVE ANALYTICS: MODELS AND METHODS

by FYJan 18, 2016

Its Hard! but AWESOME, some much info packed in a few lectures!

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 .

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 CYJul 19, 2016

Nive that the course covered a broad range of topics.

And good to get pushed to do some kaggle competition and peer review.

View all reviews

About the Data Science at Scale Specialization

Learn scalable data management, evaluate big data technologies, and design effective visualizations.

This Specialization covers intermediate topics in data science. You will gain hands-on experience with scalable SQL and NoSQL data management solutions, data mining algorithms, and practical statistical and machine learning concepts. You will also learn to visualize data and communicate results, and you’ll explore legal and ethical issues that arise in working with big data. In the final Capstone Project, developed in partnership with the digital internship platform Coursolve, you’ll apply your new skills to a real-world data science project.

Frequently Asked Questions

When will I have access to the lectures and assignments?
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:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
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.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

Practical Predictive Analytics: Models and Methods

About this Course

Skills you will gain

Instructor

Bill Howe

Offered by

University of Washington

Syllabus - What you will learn from this course

Week 1

Practical Statistical Inference

Week 2

Supervised Learning

Week 3

Optimization

Week 4

Unsupervised Learning

Reviews

TOP REVIEWS FROM PRACTICAL PREDICTIVE ANALYTICS: MODELS AND METHODS

About the Data Science at Scale Specialization

Frequently Asked Questions

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Practical Predictive Analytics: Models and Methods

About this Course

Skills you will gain

Instructor

Bill Howe

Offered by

University of Washington

Syllabus - What you will learn from this course

Week 1

Practical Statistical Inference

Week 2

Supervised Learning

Week 3

Optimization

Week 4

Unsupervised Learning

Reviews

TOP REVIEWS FROM PRACTICAL PREDICTIVE ANALYTICS: MODELS AND METHODS

About the Data Science at Scale Specialization

Frequently Asked Questions

Coursera Footer

Start or advance your career

Browse popular topics

Popular courses and articles

Earn a degree or certificate online

Coursera

Community

More