3 Days Live Virtual Training on Data Science with R Certification
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Training TypeLive Training
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CategoryR Language
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Duration12 Hours
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Rating4.9/5
Course Introduction
About the Course
The Data Science with R Certification course enables you to take your data science skills into a variety of companies, helping them analyze data and make more informed business decisions.The course covers data exploration, data visualization, predictive analytics, and descriptive analytics techniques with the R language. You will learn about R packages, how to import and export data in R, data structures in R, various statistical concepts, cluster analysis, and forecasting
Course Objective
Install R, RStudio, workspace setup, and learn about the various R packages
Gain an in-depth understanding of data structure used in R and learn to import/export data in R
Define, understand and use the various apply functions and DPLYR functions
Understand and use the various graphics in R for data visualization
Gain a basic understanding of various statistical concepts
Understand and use the hypothesis testing method to drive business decisions
Understand and use linear and non-linear regression models, and classification techniques for data analysis
Learn and use the various association rules with the Apriori algorithm
Learn and use clustering methods including k-means, DBSCAN, and hierarchical clustering
Who is the Target Audience?
This course is meant for all those students and professionals who are interested in using the R's powerful ecosystem
Basic Knowledge:
There are no prerequisites
Available Batches
| 28 Apr 2021 | Wed - Fri (3 Day) | Filling Fast10:00 AM - 02:00 PM (PT) |
| 01 Jun 2021 | Tue - Thu (03 Day) | 10:00 AM - 02:00 PM (PT) |
Pricing
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Overview
Importance of R
Data Types and Variables in R
Operators in R
Conditional Statements in R
Loops in R
R script
Functions in R
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Overview
Identifying Data Structures
Demo: Identifying Data Structures
Assigning Values to Data Structures
Data Manipulation
Demo: Assigning Values and Applying Functions
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Overview
Introduction to Data Visualization
Data Visualization Using Graphics in R
Ggplot2
File Formats of Graphic Outputs R
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Overview
Introduction to Hypothesis
Types of Hypothesis
Data Sampling
Confidence and Significance Levels
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Overview
Hypothesis Test
Parametric Test
Non-Parametric Test
Hypothesis Tests about Population Means
Hypothesis Tests about Population Variance
Hypothesis Tests about Population Proportions
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Overview
Introduction to Regression Analysis
Types of Regression Analysis Models
Linear Regression
Demo: Simple Linear Regression
Non-Linear Regression
Demo: Regression Analysis with Multiple Variables
Cross Validation
Non-Linear to Linear Models
Principal Component Analysis
Factor Analysis
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Overview
Classification and Its Types
Logistic Regression
Support Vector Machines
Demo: Support Vector Machines
K-Nearest Neighbours
Naive Bayes Classifier
Demo: Naive Bayes Classifier
Decision Tree Classification
Demo: Decision Tree Classification
Random Forest Classification
Evaluating Classifier Models
Demo: K-Fold Cross Validation
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Overview
Introduction to Clustering
Clustering Methods
Demo: K-means Clustering
Demo: Hierarchical Clustering
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Overview
Association Rule
Apriori Algorithm
Demo: Apriori Algorithm