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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:

  • Data Analysis: R is an excellent tool for data analysis, manipulation, and visualization. It has a wide range of packages and libraries that provide advanced statistical techniques, making it ideal for analyzing large and complex datasets.
  • Statistical Computing: R is designed specifically for statistical computing, making it a powerful language for statistical modeling, hypothesis testing, and machine learning.
  • Open-Source: R is an open-source language, meaning that it is free to use, and there is a large community of developers who contribute to its development. This also means that there are many free resources available for learning R.
  • Career Opportunities: R is widely used in many industries, including finance, healthcare, marketing, and technology. Therefore, learning R can help you to stand out in the job market and increase your career opportunities.
  • Integration: R can be easily integrated with other programming languages and tools, such as Python, SQL, and Excel. This makes it a versatile language that can be used in many different contexts.

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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

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