McMaster University - Experimentation for Improvement
- Offered byCoursera
- Public/Government Institute
Experimentation for Improvement at Coursera Overview
Duration | 13 hours |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Go to Website  |
Credential | Certificate |
Experimentation for Improvement at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level
- Approx. 13 hours to complete
- English Subtitles: Arabic, French, Portuguese (European), Italian, Portuguese (Brazilian), Vietnamese, German, Russian, English, Spanish
Experimentation for Improvement at Coursera Course details
- We are always using experiments to improve our lives, our community, and our work. Are you doing it efficiently? Or are you (incorrectly) changing one thing at a time and hoping for the best?
- In this course, you will learn how to plan efficient experiments - testing with many variables. Our goal is to find the best results using only a few experiments. A key part of the course is how to optimize a system.
- We use simple tools: starting with fast calculations by hand, then we show how to use FREE software.
- The course comes with slides, transcripts of all lectures, subtitles (English, Spanish and Portuguese; some Chinese and French), videos, audio files, source code, and a free textbook. You get to keep all of it, all freely downloadable.
- This course is for anyone working in a company, or wanting to make changes to their life, their community, their neighbourhood. You don't need to be a statistician or scientist! There's something for everyone in here.
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- Over 1500 people have completed this online course. What have prior students said about this course?
- "This definitely is one of the most fruitful courses I have participated at Coursera, considering the takeaways and implementations! And so far I finished 12 [courses]."
- "Excelente curso, flexible y con suficiente material didáctico fácilmente digerible y cómodo. No importa si se tiene pocas bases matemáticas o estadísticas, el curso proporciona casi toda explicación necesaria para un entendimiento alto."
- "I wish I had enrolled in your course years ago -- it would have saved us a lot of time in optimizing experimental conditions." Jason Eriksen, 3 Jan 2017
- "Interesting and developing both analytical and creative thinking. The lecturer took care to bring lots of real live examples which are fun to analyze." 20 February 2016.
- "... love your style of presentation, and the examples you took from everyday life to explain things. It is very difficult to make such a mathematical course accessible and comprehensible to this wide a variety of people!"
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Experimentation for Improvement at Coursera Curriculum
Introduction
Promotional video for this course
1A: Why experiments are so important
1B: Some basic terminology
1C: Analysis of your first experiment
1D: How NOT to run an experiment
Materials for this section
Ungraded practice quiz 1
Module 1 quiz
Analysis of experiments by hand
2A: Analysis of experiments in two factors by hand
2B: Numeric predictions from two-factor experiments
2C: Two-factor experiments with interactions
2D: In-depth case study: analyzing a system with 3 factors by hand
Enrichment: Made for you by Madeleine: an interview with Joy
Materials for this section
Ungraded practice quiz 2
Module 2 quiz
Using computer software to analyze experiments
3A: Setting up the least squares model for a 2 factor experiment
3B: Solving the mathematical model for a 2 factor experiment using software
3C: Using computer software for a 3 factor experiment
3D: Case study: a 4-factor system using computer software
Enrichment: Dr. Soo Chan Carusone talks about experiments in a medical context
Materials for this section
Ungraded practice quiz 3
Module 3 quiz
Getting more information, with fewer experiments
4A: The trade-offs when doing half-fraction factorials
4B: The technical details behind half-fractions - math warning!
4C: A case study with aliasing in a fractional factorial
4D: All about disturbances, why we randomize, and what covariates are
4E: All about blocking
4F: Introducing aliasing notation
4G: Using aliasing notation to plan experiments
4H: An example of an analyzing an experiment with aliasing
Enrichment: My colleague, David, and his student Jeff, talk about water treatment experiments
Materials for this section
Ungraded practice quiz 4: [4A,B,C,D]
Ungraded practice quiz [4E, 4F, 4G, 4H]
Module 4 quiz [4A to 4H]
Response surface methods (RSM) to optimize any system
5A: Response surface methods (RSM): an introduction
5B: Response surface methods (RSM): one variable
5C: Why changing one factor at a time (OFAT) will mislead you
5D: The concept of contour plots and which objectives should we maximize
5E: RSM in 2 factors: introducing the case study
5F: RSM case study continues: constraints and mistakes
5G: RSM case study continues: approaching the optimum
Enrichment: An interview with Dr. Joe Kim (McMaster University)
Materials for this section
Ungraded practice quiz [5A, 5B, 5C, 5D]
Module 5 quiz [5A, 5B, 5C, 5D]
Ungraded practice quiz [5E, 5F, 5G]
Module 5 quiz [5E, 5F, 5G]
Wrap-up and future directions
6: The big picture (wrapping it up, and other topics)
Materials for this section
Final survey: your feedback and comments
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