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:
1. Design effective experiments and analyze the results
2. Use resampling methods to make clear and bulletproof statistical arguments without invoking esoteric notation
3. Explain and apply a core set of classification methods of increasing complexity (rules, trees, random forests), and associated optimization methods (gradient descent and variants)
4. Explain and apply a set of unsupervised learning concepts and methods
5. Describe the common idioms of large-scale graph analytics, including structural query, traversals and recursive queries, PageRank, and community detection
This course is part of the Data Science at Scale Specialization
Practical Predictive Analytics: Models and Methods
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About this Course
4.1
287 ratings
Skills you will gain
Random ForestPredictive AnalyticsMachine LearningR Programming
Syllabus - What you will learn from this course
Week
12 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.
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
Week
22 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.
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 Microbes28m
Week
31 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.
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
Week
42 hours 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.
4 videos (Total 21 min), 1 quiz
4 videos
K-means5m
DBSCAN4m
DBSCAN Variable Density and Parallel Algorithms4m
4.1
53 Reviews25%
started a new career after completing these courses
20%
got a tangible career benefit from this course
Top Reviews
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.
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 .
Instructor
Bill Howe
Director of ResearchScalable Data Analytics
About 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.
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?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
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.
What is the refund policy?
Is financial aid available?
More questions? Visit the Learner Help Center.
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