3 Days Live Virtual Training on Data Science with R Certification

Get an understanding of Data Science with R Programming, right from the foundations, from this course.

Duration: 3 Days

Hours: 15 Hours

Training Level: All Level

Virtual Class Id: 50027

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 various data structures in R, various statistical concepts, cluster analysis, Regression and classification. In this curriculum we will have in-depth mathematical understanding of the algorithms from the basics.

Course Objective:

Install R, Rstudio, 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 functions in R

Learn to do data visualization using ggplot2 packages

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

Curriculum

Total Duration: 15 Hours

Introduction to R

Various datatypes in R

Vectors

Matrices

Data Frames

Core programming concepts

While Loops

For Loops
If Else statements

Visualizations in R
Packages in R

ggplot

dfply
e1071

Matrix operations
Dataframes

Joins and manipulations in Dataframes

Machine Learning

Data Pre-processing

Missing Data
Categorical Data
Feature Scaling
Data Split (Test and Training Set)

Regression

Simple Linear Regression
Multiple Linear Regression

Classification

Logistic Regression
K Nearest Neighbours (K-NN)
Support Vector Machine (SVM)
Navie Bayes
Decision Tree Classification
Random Forest Classification
XGBoost
Regularization in Logistic Regression
Understanding different hyper parameters
Accuracy measures

Clustering

K Means
Hierarchical Clustering
DBScan

Association Rule Mining

Apriori

Model selection and Boosting

K Fold Technique
Grid Search