Data Science with R

Live Online (VILT) & Classroom Corporate Training Course

This interactive and comprehensive course is a great place for attendees to get started on R programming language and its use in Data Science.
Data Science with Python

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Overview

Data Science with R course covers topics like exploratory data analysis, statistics fundamentals, hypothesis testing, regression & classification modeling techniques and machine learning algorithms. Participants will learn how to create R programs that will help discover and interpret relationships in complex information and solve real world problems.

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Objectives

At the end of Data Science with R training course, participants will

  • Get acquainted with various analysis and visualization tools such as Ggplot and plotly
  • Understand the behavior of data; build significant models to understand Statistics Fundamentals
  • Learn about the various R libraries like Dplyr, Data.table used to manipulate data
  • Use R libraries and work on data manipulation, data preparation and data explorations
  • Use of R graphics libraries like Ggvis, Plotly etc.
  • Learn Supervised and Unsupervised Machine Learning Algorithms
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Prerequisites

Participants are expected to have basic programming knowledge.

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Course Outline

  • What is Data Science?
  • Analytics Landscape
  • Life Cycle of a Data Science Project
  • Data Science Tools & Technologies

  • Intro to R Programming
  • Installing and Loading Libraries
  • Data Structures in R
  • Control & Loop Statements in R
  • Functions in R
  • Loop Functions in R
  • String Manipulation & Regular Expression in R
  • Working with Data in R
  • Data Visualization in R
  • Case Study

  • Measures of Central Tendency
  • Measures of Dispersion
  • Descriptive Statistics
  • Probability Basics
  • Marginal Probability
  • Bayes Theorem
  • Probability Distributions
  • Hypothesis Testing

  • ANOVA
  • Linear Regression (OLS)
  • Case Study: Linear Regression
  • Principal Component Analysis
  • Factor Analysis
  • Case Study: PCA/FA

  • Logistic Regression
  • Case Study: Logistic Regression
  • K-Nearest Neighbor Algorithm
  • Case Study: K-Nearest Neighbor Algorithm
  • Decision Tree
  • Case Study: Decision Tree

  • Understand Time Series Data
  • Visualizing TIme Series Components
  • Exponential Smoothing
  • Holt’s Model
  • Holt-Winter’s Model
  • ARIMA
  • Case Study: Time Series Modeling on Stock Price
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