IIM Kozhikode - Professional Certificate Programme in Advanced Data Analytics for Managers 

Introduction R environment

IDE-R studio

Installing packages and loading packages in R

Creating variables

Scalars, vectors & matrices

List, data frames & data types

Converting between vector types

Cbind & Rbind

Attach and detach functions

Reading .csv and .txt files

Importing data from excel

Loading and storing data with a clipboard

Saving in R data, loading R data objects

Writing data into the file

Writing text and output from analyses to file

Rmarkdown

Data subsets

Selecting rows/observations

Rounding a number

Creating a string from variable

Factor labels

Selecting columns/fields

Merging data

Relabelling the column names

Data sorting, data aggregation, and finding and removing duplicate records

Application of dplyr package (select, arrange, mutate, aggregate, summarise, and group)

Basics of data visualisation using ggplot2

Aesthetic mappings

Common problems

Facets

Geometric objects

Position adjustments

Coordinate systems

The layered grammar of graphics

Combining plots

Execution of various types of plots (box plot, histogram, pie chart, line chart, scatterplot, word cloud, probability plots, mosaic plots, correlograms, and interactive graphs)

Data cleaning

Handling missing data

Data imputation

Feature filtering

Categorical feature filtering

Identifying misclassifications

Data transformation

Min-max normalisation

Z-score

Standardization

Decimal scaling

Transformations to achieve normality

Outliers

Graphical methods for identifying outliers

Numerical methods for identifying outliers

Flag variables

Transforming categorical variables into numerical

variables

Binning numerical variables reclassifying categorical

variables

Adding an index field

Removing variables that are not useful

Data balancing techniques

Hypothesis testing versus exploratory data analysis

Getting to know the data set

Exploring categorical variables

Exploring numeric variables

Exploring multivariate relationships

Selecting interesting subsets of the data for further

investigation

Using EDA to uncover anomalous fields

Binning based on predictive value

Deriving new variables: flag variables

Deriving new variables: numerical variables

Using EDA to investigate correlated predictor variables

Need for dimension-reduction in data mining

Principal components analysis (PCA)

Application of PCA

Statistical inference

Confidence interval estimation of the mean

The margin of error

Confidence interval estimation of the proportion

Hypothesis testing for the mean

Assessing the strength of evidence against the null

hypothesis

Using confidence intervals to perform hypothesis tests

One-sample t-test

Paired sample t-test

Chi-square test for goodness of fit of multinomial data

Analysis of variance (ANOVA)

Supervised versus unsupervised methods

Statistical methodology and data mining methodology

Cross-validation

Overfitting

Bias-variance trade-off

Balancing the training data set

Establishing baseline performance

Simple regression analysis

Model formulation

Verifying the regression assumptions

Inference in regression

Multiple regression analysis

Dummy variable

Stepwise regression analyze

k-nearest neighbour algorithm

Decision tree

Random forest

Neural networks for estimation and prediction

Application of logistic regression for estimation and prediction

Naive bayes and Bayesian networks

Hierarchical Clustering Methods

k-Means Clustering

Measuring Cluster Goodness

Affinity Analysis

Market Basket Analysis

Text mining and sentiment analysis

Social media analytics (Twitter)

Lexicon analysis

Social network analysis