This course is part of the Data Science at Scale Specialization

4.1
stars
309 ratings

Bill Howe

35,460 already enrolled

Offered By

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

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Coursera Labs

Includes hands on learning projects.

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Course 2 of 4 in the

Data Science at Scale Specialization

Approx. 6 hours to complete

English

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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.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 2 of 4 in the

Data Science at Scale Specialization

Approx. 6 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructor

Bill Howe

Director of Research

Scalable Data Analytics

84,900 Learners

4 Courses

Offered by

University of Washington

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Syllabus - What you will learn from this course

Week1

Week 1

2 hours to complete

Practical Statistical Inference

2 hours to complete

28 videos (Total 121 min)

28 videos

Appetite Whetting: Bad Science5mHypothesis Testing5mSignificance Tests and P-Values3mExample: Difference of Means4mDeriving the Sampling Distribution6mShuffle Test for Significance4mComparing Classical and Resampling Methods3mBootstrap6mResampling Caveats6mOutliers and Rank Transformation3mExample: Chi-Squared Test3mBad Science Revisited: Publication Bias4mEffect Size4mMeta-analysis5mFraud and Benford's Law4mIntuition for Benford's Law2mBenford's Law Explained Visually3mMultiple Hypothesis Testing: Bonferroni and Sidak Corrections3mMultiple Hypothesis Testing: False Discovery Rate4mMultiple Hypothesis Testing: Benjamini-Hochberg Procedure3mBig Data and Spurious Correlations4mSpurious Correlations: Stock Price Example3mHow is Big Data Different?3mBayesian vs. Frequentist4mMotivation for Bayesian Approaches3mBayes' Theorem2mApplying Bayes' Theorem4mNaive Bayes: Spam Filtering4m

Week2

Week 2

3 hours to complete

Supervised Learning

3 hours to complete

26 videos (Total 111 min), 1 reading, 1 quiz

26 videos

Statistics vs. Machine Learning4mSimple Examples3mStructure of a Machine Learning Problem5mClassification with Simple Rules5mLearning Rules4mRules: Sequential Covering3mRules Recap2mFrom Rules to Trees2mEntropy4mMeasuring Entropy4mUsing Information Gain to Build Trees6mBuilding Trees: ID3 Algorithm2mBuilding Trees: C.45 Algorithm4mRules and Trees Recap3mOverfitting7mEvaluation: Leave One Out Cross Validation5mEvaluation: Accuracy and ROC Curves5mBootstrap Revisited4mEnsembles, Bagging, Boosting4mBoosting Walkthrough5mRandom Forests3mRandom Forests: Variable Importance5mSummary: Trees and Forests2mNearest Neighbor4mNearest Neighbor: Similarity Functions4mNearest Neighbor: Curse of Dimensionality3m

1 reading

R Assignment: Classification of Ocean Microbes10m

1 practice exercise

R Assignment: Classification of Ocean Microbes30m

Week3

Week 3

1 hour to complete

Optimization

1 hour to complete

11 videos (Total 41 min)

Week4

Week 4

1 hour to complete

Unsupervised Learning

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 DSJul 2, 2020

Hands on practices are very good. learning predictive model was a challenge.

by GJJul 16, 2021

This course helpemd me understand more about machine learning and a set of tools to help with the same.

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 KRNov 10, 2015

Very nice assignments and content. You learn a lot when you complete all assignments.

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.

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Practical Predictive Analytics: Models and Methods

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Skills you will gain