# 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.

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## Data Science with R ### 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. ### 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 ### Prerequisites

Participants are expected to have basic programming knowledge. ### Course Outline

Introduction to Data Science2021-06-30T13:16:30+05:30

#### Introduction to Data Science

• What is Data Science?
• Analytics Landscape
• Life Cycle of a Data Science Project
• Data Science Tools & Technologies
Mastering R2021-06-30T13:17:11+05:30

#### Mastering R

• Intro to R Programming
• 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
Probability & Statistics2021-06-30T13:18:00+05:30

#### Probability & Statistics

• Measures of Central Tendency
• Measures of Dispersion
• Descriptive Statistics
• Probability Basics
• Marginal Probability
• Bayes Theorem
• Probability Distributions
• Hypothesis Testing
Advanced Statistics & Predictive Modeling – I2021-06-30T13:18:19+05:30

#### Advanced Statistics & Predictive Modeling – I

• ANOVA
• Linear Regression (OLS)
• Case Study: Linear Regression
• Principal Component Analysis
• Factor Analysis
• Case Study: PCA/FA
Advanced Statistics & Predictive Modeling – II2021-06-30T13:18:36+05:30

#### Advanced Statistics & Predictive Modeling – II

• Logistic Regression
• Case Study: Logistic Regression
• K-Nearest Neighbor Algorithm
• Case Study: K-Nearest Neighbor Algorithm
• Decision Tree
• Case Study: Decision Tree
Time Series Forecasting2021-06-30T13:19:07+05:30

#### Time Series Forecasting

• 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
2023-01-06T14:06:50+05:30