Location:Main Road, Bangalore


Projects In Data Science Using R




Data Science Online Institute


Working Professionals and Freshers


Both Classroom and Online Classes


Week Days and Week Ends

Duration :

Fast Track and Regular 60 Days

Data Science Objectives

•Learn about Data Science Practices and guidelines.
•What are the advantages of Data Science?
•You will learn basics of programming in Data Science
•Learn about each and every major Data Science component.
•Learn Data Science from Scratch with Demos and Practical examples.
•Learn how to model in Data Science with no previous experience
•Learn How to code in Data Science in simple and easy way.
•Learn the core fundamentals of Data Science to fast-track your development process
•Learn the fundamentals of the Data Science using both a theoretical and practical approach

projects in data science using r Course Features

•Advanced Topics covered with examples
•Course delivery through industry experts
•We assist on Internship on Real-Time Project 
•Create hands-on projects at the end of the course
•We Also provide Case studies for Online Training Courses
•Project manager can be assigned to track candidates’ performance
•Live project based on any of the selected use cases, involving implementation of the concepts
• Our dedicated HR department will help you search jobs as per your module & skill set, thus, drastically reducing the job search time

Who are eligible for Data Science

•c++, React.js, Java Fullstack, Core Java Data Structure, Java Micro-services, Devops, Microsoft Azure, Cloud Computing, Machine Learning, Automation Testing
•Db2 Dba, SQL Dba, Java Developers, Java Lead, Firmware Developer, asp.net developer, Device Driver Developer, Hybris Developer, e Commerce Architect, ATG, WCS
•Javascript, Node.Js, Algorithms, Web Technologies, Web Server, Cloud Computing, HP Data Protector, Technical Skills, Problem Solving
•Sap, Process Executive, Hadoop Developer, Hadoop Architect, Sap Srm/snc Testing, Sap Pp / Qm Testing, Sap Ewm Testing, Sharepoint Developer, T24 Technical And
•Web application developer, .Net Developer, PHP Developer, Seo Analyst, Associate Designer, Ui Designer, senior .net Developer, .Net TL, Analytic Engineer


Introduction to Data Science Using R
•Intro to R studio
•The Assignment Operator
•Basic Data Types in R
•Matrices and Data Frames
•Subsetting Syntax
•Project 1 : Introduction to R – Problem Statement
•Project 1 Solution
•Data Transformation
•Data Transformations on Rows
•Data Transformations on Columns
•Data Transformations on Iris Dataset – Project Problem Statement
•Data Transformations on Iris Dataset – Project Solution
•Wide and Long Data
•Grouped Transposes
•Project 2 : Wide and Long Data – Problem statement
•Project 2 Solution
•What are Joins
•Programming Joins Part 1
•Programming Joins Part 2
•Project 3 :Performing Joins – Problem Statement
•Project 3 Solution
•Data Visualization
•GGPLOT Basics
•Aesthetic Mappings in GGPLOT
•Facets in GGPLOT
•Geoms in GGPLOT
•Statistical Transformations in GGPLOT
•Project 4 : GGPLOT – Problem Statement
•Project 4 Solution
•Project 5: Facets, Geoms and Tansformations
•Project 5 Solution
•Exploratory Data Analysis
•How to Identify Missing Values
•How to Identify Outliers
•What to do with Missing Values and Outliers
•Functional Transformations
•Regression Models
•Intro to Regression Problem and Data Set
•Correlations and Final Data Set
•What is Multiple Regression
•Building a Multiple Regression Model
•Measuring Regression Model Accuracy
•KNN Model
•What is KNN
•Building a KNN Model
•Assessing KNN Model Performance
•Assessing Training and Test Error for KNN
•What is a Decision Tree
•Creating a Decision Tree
•Assessing Performance of a Decision Tree
•Model Comparison
•Project: Build a model that is better than our multiple regression and KNN model
•Classification Dataset
•Intro to Classification Dataset and Problem
•EDA Part 1
•EDA Part 2
•What is Logistic Regression
•Building a Logistic Regression Model
•Building a Classification Tree
•Building a Random Forest
•Project: Build a model better than logistic regression, decision and RF model