R programming is a language and environment for statistical computing and graphics. It is widely used for data analysis, statistical modeling, visualization, and machine learning. R is an open-source language and has a large community of users who contribute packages to extend its functionality.
R is important for data analysis for several reasons:
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CURRICULUM
Introduction to R
- History and evolution of R
- Principle and software paradigm
- Description of R interface
- Advantages of R
- Drawbacks of R
- So why use R?
- References for learning
Advance Data Manipulation in R (Packages like DPLYR, PLYR, SQLDF, MASS)
- Importing and exporting data from .txt files and .xls-like files
- Advanced data manipulation
- Accessing variables and management of subsets in data
- Working with characters, text and dates
Logical FormulasExploratory Analysis & Data Visualization
- Graphics for basic descriptive statistics
- Graphics for time-related data
- Introduction to more advanced graphics
- Adding relevant information & customize your graphics
Fundamental of Statistics
- Types of Variables, measures of central tendency and dispersion
- Variable Distributions and Probability Distributions
- Normal Distribution and Properties
- Central Limit Theorem and Application
Basic Statistical Analysis
- Statistics Basics Introduction to Data Analytics, descriptive and summary
- Inferential statistics
Statistical Significant Tests
- Hypothesis Testing Null/Alternative Hypothesis formulation
- Z‐Test, T‐Test, Chi‐Square test
- Analysis of Variance (ANOVA)
- Chi Square Test
- Correlation
Data Preparation
- Need for data preparation
- Outlier treatment
- Missing values treatment
- Multicollinearity
Predictive modeling & Time Series Analysis
- Basics of regression analysis
- Linear regression
- Logistic regression
- Interpretation of results
- Multivariate Regression modeling
Machine Learning Algorithm
- Text Analytics
- Random Forest
- Support Vector Machine (SVM)
- Naïve Bayes Algorithm
- K-NN Classification & Regression
Case Study Project:
- Customer Marketing Response Predictive Modeling
- Patient Satisfaction Analysis
- Call Centre Effectiveness Predictive Modeling
- Customer Segmentation for Cross Sell-UpSell Modeling