NOC:Dealing with Materials Data: Collection, Analysis and Interpretation

Lecture 1 - Descriptive Statistics - I

Lecture 2 - Descriptive Statistics - II

Lecture 3 - Probability and Distribution

Lecture 4 - Random variable and Expectation - I

Lecture 5 - Random variable and Expectation - II

Lecture 6 - Random variable and Expectation - III

Lecture 7 - Random variable and Expectation - IV

Lecture 8 - Module: Introduction to R

Lecture 9 - R:Demos and getting help

Lecture 10 - R as calculator and plotter: Diffusivity, scaled temperatures

Lecture 11 - R as calculator and plotter: Diffraction, configurational entropy

Lecture 12 - Data in tabular form: Properties of elements

Lecture 13 - Tabular data in R: alternate methodology

Lecture 14 - Dataframe in R: Properties of elements

Lecture 15 - R libraries for plotting

Lecture 16 - Importing and plotting data

Lecture 17 - Property charts: Importing and plotting data

Lecture 18 - Introduction to R: Summary of the module

Lecture 19 - Descriptive statistics

Lecture 20 - Presenting experimental results: Data on conductivity of ETP copper

Lecture 21 - Property based reports, errors, significant digits

Lecture 22 - Dealing with distributions: Grain size data

Lecture 23 - Grain size data: Property and rank based reports

Lecture 24 - Case study: Grain size in a two phase steel

Lecture 25 - Grain size in a two phase steel: Descriptive statistics

Lecture 26 - Presenting experimental results: data with error bars

Lecture 27 - Errors and their propagation

Lecture 28 - Fitting experimental data to distributions

Lecture 29 - Combining uncertainties

Lecture 30 - Summary:Descriptive statistics

Lecture 31 - Special Random Variables - I

Lecture 32 - Special Random Variables - II

Lecture 33 - Special Random Variables - III

Lecture 34 - Special Random Variables - IV

Lecture 35 - Special Random Variables - V

Lecture 36 - Probabilty Plots

Lecture 37 - Probability distributions

Lecture 38 - Properties of probability distributions

Lecture 39 - Bernoulli trials and binomial distributions

Lecture 40 - Atom probe technique and negative binomial distribution

Lecture 41 - Atom probe and hypergeometric distribution

Lecture 42 - Atom probe: analysis of error

Lecture 43 - Nucleation and Poisson distribution

Lecture 44 - Normal distribution

Lecture 45 - Normal distribution and error function

Lecture 46 - Probability scale

Lecture 47 - Sampling Distribution - I

Lecture 48 - Sampling Distribution - II

Lecture 49 - Sampling Distribution - III

Lecture 50 - Parameter Estimation - I

Lecture 51 - Parameter Estimator - II

Lecture 52 - Parameter Estimator - III

Lecture 53 - Parameter Estimator - IV

Lecture 54 - Bayesian Estimation

Lecture 55 - Log normal distribution

Lecture 56 - Lorentz/Cauchy distribution

Lecture 57 - Lifetime and exponential distributions

Lecture 58 - Distributions from statistical mechanics

Lecture 59 - Uniform distribution and summary of probability distributions

Lecture 60 - Data processing: Introduction

Lecture 61 - Distribution function of a data series

Lecture 62 - Estimating mean and mean-square-deviation of data

Lecture 63 - Data with unequal weights

Lecture 64 - Robust estimates

Lecture 65 - From data to underlying distribution

Lecture 66 - Bootstrap method

Lecture 67 - Summary:Data processing

Lecture 68 - Hypothesis Testing - I

Lecture 69 - Hypothesis Testing - II

Lecture 70 - Hypothesis Testing - III

Lecture 71 - Hypothesis Testing - IV

Lecture 72 - Hypothesis Testing - V

Lecture 73 - Hypothesis Testing - VI

Lecture 74 - Graphical handling of data

Lecture 75 - Fitting and graphical handling of data: Introduction

Lecture 76 - Data transformable to linear

Lecture 77 - Data of known functional form

Lecture 78 - Calibration,Fitting, Hypotheses testing

Lecture 79 - Analysis of variance

Lecture 80 - Summary:Fittng and graphical handling of data

Lecture 81 - Regression Analysis - I

Lecture 82 - Regression Analysis - II

Lecture 83 - Regression Analysis - III

Lecture 84 - Regression Analysis - IV

Lecture 85 - Analysis of Variance - I

Lecture 86 - Analysis of Variance - II

Lecture 87 - Design of Experiment - I

Lecture 88 - Design of Experiment - II

Lecture 89 - Design of Experiment - III

Lecture 90 - Design of Experiment - IV

Lecture 91 - Summary of the course

Lecture 92 - Case studies: Introduction

Lecture 93 - Case study 1: Data smoothing - I

Lecture 94 - Case study 1: Data smoothing - II

Lecture 95 - Case study 2: Error analysis

Lecture 96 - Case study 3: Calibration

Lecture 97 - Case study 4: Design of experiment

Lecture 98 - Case study 5: Hypothesis testing

Lecture 99 - Course summary