Table of Contents:
00:00 Lecture 1. Where do data come from?
01:45 Outline
02:48 A short history of data
06:07 Sensors and data
08:03 Outline
08:48 Time dependent dielectric breakdown
13:28 Voltage-dependence of Dielectric Breakdown
16:06 Weibull Distributed Failure times
17:58 Predictions based on data
19:02 Issues with small data
21:59 Outline
22:25 Big vs. small data
24:54 Small vs. big data
26:09 Where do data come from?
27:15 Repository of big data
28:36 ……..driven by memory technology
29:53 Outline
30:09 Outline
30:10 What to expect ….
35:07 Outline
36:54 Outline of the course
42:21 Reference Books
43:54 Few other information
44:59 Conclusions

This video is part of the course An Introduction to Data Analysis, Design of Experiment, and Machine Learning by Ashraf Alam at Purdue University. The course can be found on nanoHUB.org at https://nanohub.org/resources/28817 where other downloads are available.

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

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