nanoHUB.org

Fall 2018
This course is part of Purdue’s new “Breadth at the Edges” initiative.

This teaching is a preview of the course to be offered in the Fall of 2019

This course will provide the conceptual foundation so that a student can use modern statistical concepts and tools to analyze data generated by experiments or numerical simulation. We will also discuss principles of design of experiments so that the data generated by experiments/simulation are statistically relevant and useful. We will conclude with a discussion of analytical tools for machine learning and principle component analysis. At the end of the course, a student will be able to use a broad range of tools embedded in MATLAB and Excel to analyze and interpret their data.

Topics Covered:

• Review of Basic Statistical Concepts
• Where do data come from: Big vs. Small Data
• Collecting and Plotting Data: Principles of Robust Data Analysis
• Physical vs. Empirical Distribution
• Model Selection and Goodness of Fit
• Scaling Theory of Design of Experiments
• Statistical Theory of Design of Experiments
• Machine Learning vs. Physics-based Machine Learning

Breadth at the Edges initiative:

This course is part of a Purdue University initiative that aims to complement the expertise that students develop with the breadth at the edges needed to work effectively in today's multidisciplinary environment. These serious, short courses require few prerequisites and provide a general framework that can be filled in with self-study when needed.

Recommended Text(s):

• Applied Statistics and Probability for Engineers, 3rd Edition, Montomery and Runger, Wiley, 2003.
• Understanding Robust and Exploratory Data Analysis, D. C. Hoaglen, F. Mosteller and J.W. Tukey, Wiley Interscience, 1983.
• Video lectures by Stuart Hunter (Available on Youtube).

Researchers should cite this work as follows:

• Muhammad A. Alam (2019), "ECE 695E: An Introduction to Data Analysis, Design of Experiment, and Machine Learning," https://nanohub.org/resources/28817.

  BibTex | EndNote

1. big data
2. data analysis
3. data collection
4. design of experiments
5. error
6. joes picks
7. machine learning
8. Model selection
9. NCN Group - Machine Learning
10. physics-based machine learning
11. PURDUE ECE Breath at the Edges initiative
12. scaling theory