PROGRAM DETAILS

• Module 01: Introduction to R Programming
• The Importance of Statistics and Data Analysis
• Future of Statistics
• Interdisciplinary Applications
• Features of R programming
• Statistical Exploration and Visualization
• Comparison between R and Python
• R Advantages and Disadvantages
• Career Opportunities
• Module 02: R Programming for Data Science
• Overview to R
  
• History, purpose and Overview of R.
• Installing R and RStudio.
• Overview of the RStudio layout.
• Exploring the console, script editor, environment, and plots panel.
• Basic Operations:
  
• Operators (Arithmetic Operators, Relational Operators, Logical Operators, Assignment Operators, Miscellaneous Operators)
• Variable Assignment (Data Type of a Variable, Finding Variables, Deleting Variables)
• Data types (Integer, numeric, character, logical, Complex)
• Vectors (Vector Creation, Accessing Vector Elements, Vector Manipulation)
• Matrices and Arrays (Creating matrices and arrays, Matrix Computations)
• Lists (Creating and working with lists, Accessing list elements, Merging Lists, Converting List to Vector)
• Data Frames (Extract Data from Data Frame, Expand Data Frame)
• Factors (Factors in Data Frame, Changing the Order of Levels, Generating Factor Levels)
• Data Reshaping (Joining Columns and Rows in a Data Frame, Merging Data Frames, Melting and Casting, Melt the Data, Cast the Molten Data)
• Decision Making (If Statement, If...Else Statement, The if...else if...else Statement, Switch Statement)
• Loops (Repeat Loop, While Loop, For Loop, Loop Control Statements, Break Statement, Next Statement) 
• Module 03: Data Input and Visualization
• Data Import and Export: 
• Reading and writing data from/to various formats (CSV, Excel, Web Data etc.)
• Data Cleaning and Transformation: 
• Dealing with missing values; 
• Removing duplicates; 
• Transforming and recoding variable names
• Basic Plotting: 
• Creating scatter plots, bar plots, pie plots, histograms, and line plots using base R
•  Adding titles, labels, and legends.
• Introduction to Packages: 
• Installing and loading packages 
• Utilizing popular packages (ggplot2, tydiverse, dplyr, tidyr) for data manipulation. 
• Module 04: Introduction to Descriptive Statistics
• Measures of central tendency: 
• Mean
• Median
• Mode
• Measures of dispersion: 
• Range 
• Variance
• Standard deviation
• Module 05: Statistics with Correlation and Regression
• Regression Analysis 
• Linear
• Multiple
• Logistic
• Poisson
• Normal Distribution 
• ANOVA (Analysis of Variance) Test (One Way, Two Way)
• Covariance and Correlation
• Skewness and Kurtosis
• Hypothesis Testing 
• One Sample T-Testing
• Two Sample T-Testing
• Paired Sample T-test 
• Covariance Matrix
• Pearson Correlation
• Normal Probability Plot
• Quantile-Quantile (Q-Q) plots
• Module 06: Advance and Attractive Presentation of Data (ggplo2)
  
• Introduction with ggplot2 package
• Grammar of ggplot2
• Scatter plots
•  Box plots
• Dot plot
• Violin plots
• Heat map
• Histogram
• Density plots
• Module 07: Time Series Analysis
• Understanding time series data and its components.
• Decomposition of time series: trend, seasonality, random and observed
  Autocorrelation Function (ACF)
• Partial Autocorrelation Function (PACF)
• Forecasting techniques
• Moving averages
• Exponential smoothing and
• ARIMA Model
• Module 08: Machine Learning Model
  
• Decision Tree
• Random Forest
• K-Means Clustering
• Evaluation Matrix (Precision, Recall and F1-Score using R)
• BONUS
  
• Multivariate analysis techniques
• PCA (Principal Component Analysis)
• Factor analysis.
  
• Taylor Diagram