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Course Objectives

  • Understand and apply R programming for enterprise analytics.
  • Explore and prepare data in R
  • Perform exploratory and statistical analytics in R
  • Create visualization graphics using R plotting package
  • Implement predictive and prescriptive models in R

Description

Businesses and governments are finding ways to make sense of all the available data in this big data era. Data Analytics thus finds favor as it utilizes tools and techniques like data mining, pattern matching, data visualizations and predictive modeling to predict and optimize outcomes and derive value from the data. Equipped with this useful information, organizations can compete better in cut-throat markets both locally and globally.

Today, data is everywhere. We create it simply with the touch of a button. But how much of it is actually useful? Whether you are in finance, operations, sales & marketing or planning, you may be in touch with millions of data points every day without being aware of how to derive valuable information from this data.

Target Audience

This course is ideal for professionals who intend to work on data analytics applications using R. It provides a comprehensive guide to implementing important data analytics components using the R programming language. It features a series of hands-on exercise to ensure participants get enough familiarity working in the R environment and at the same time, experience interesting data analytics topics. These hands-on exercises are carried out using commonly seen use cases across different industries as examples, such that participants will be able to relate these knowledge with the requirements in respective workplaces.

Training Outline

Day 1
  • Overview of Data Analytics
    • Data Analytics – Trending Now
    • The Relationship and Impact of Data to
      Businesses
    • Descriptive vs Predictive Analytics
    • What is R?
    • Why use R?
  • R Programming Basics
    • Installing R and R Studio
    • Getting Started with R Console and R Studio
    • Introduction to R packages
    • Data Types and Data Structures
    • Basic R Commands and Common Syntax
    • R Scripts and User Defined Functions
    • Flow Control
    • Debugging
Day 2
  • • Data Preparation in R
    • Importing Libraries
    • Importing Data from Files and Database
    • Continuous and Categorical Variables
    • Date and Time Variables
    • Data Merging
    • Data Transformation
    • Handling Missing Data (Imputation)
    • Handling Anomalous Values
  • Exploratory Data Analysis
    • Obtaining Descriptive Statistics in R
    • Creating Data Visualization using the ggplot package
    • Types of Variables: Qualitative and
      Quantitative Analysis
    • Number of Variables: Univariate, Bivariate and Multivariate
Day 3
  • Introduction to Predictive Analytics
    • Concepts of Supervised and Unsupervised Learning
    • Data Splitting (Training and Testing)
    • Feature Scaling
    • A Basic Model: Linear Regression
    • Cross Validation
  • Implementation of Regression Methods in R
    • Linear Regression
    • Support Vector Regression (SVR)
    • Decision Tree Regression
    • Ensemble Method: Random Forest
    • Evaluation of Regression Model Performance
Day 4
  • Implementation of Classification Methods in R
    • Logistic Regression
    • K-Nearest Neighbor (k-NN)
    • Support Vector Machine (SVM)
    • Decision Tree
    • Ensemble Method: Random Forest
    • Evaluation of Classification Model
      Performance
  • Implementation of Unsupervised Methods in R
    • K-means Clustering
    • Hierarchical Clustering
  • Dimensionality Reduction
    • Principal Component Analysis (PCA)
    • Linear Discriminant Analysis (LDA)
  • Introduction to Prescriptive Analytics
    • Delivering Business Values with Predictive Models
    • Optimization Packages in R
    • Formulation of Objective Functions and
      Constraints
    • Selecting and Applying Solvers

Prerequisite

Basic programming knowledge.