NOC:Dealing with Materials Data: Collection, Analysis and Interpretation (USB)

Media Storage Type : 64 GB USB Stick

NPTEL Subject Matter Expert : Dr. M.P. Gururajan

NPTEL Co-ordinating Institute : IIT Bombay

NPTEL Lecture Count : 99

NPTEL Course Size : 46 GB

NPTEL PDF Text Transcription : Available and Included

NPTEL Subtitle Transcription : Available and Included (SRT)

Lecture Titles:

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