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.
Offered By
Data Science at Scale SpecializationUniversity of WashingtonAbout this Course
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Learner Career Outcomes
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started a new career after completing these courses
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got a tangible career benefit from this course
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Earn a Certificate upon completion
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Course 2 of 4 in the
Flexible deadlines
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Approx. 7 hours to complete
English
Subtitles: French, Portuguese (European), Korean, Russian, English, Spanish
Skills you will gain
Random ForestPredictive AnalyticsMachine LearningR Programming
Learner Career Outcomes
25%
started a new career after completing these courses
20%
got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 2 of 4 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
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)
28 videos
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
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
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
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)
11 videos
Gradient Descent Visually4m
Gradient Descent in Detail2m
Gradient Descent: Questions to Consider3m
Intuition for Logistic Regression4m
Intuition for Support Vector Machines3m
Support Vector Machine Example3m
Intuition for Regularization3m
Intuition for LASSO and Ridge Regression3m
Stochastic and Batched Gradient Descent5m
Parallelizing Gradient Descent3m
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)
4 videos
K-means5m
DBSCAN4m
DBSCAN Variable Density and Parallel Algorithms4m
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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