R Programming

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Course Description

Educonverge provides the live online training on R Programming course in Delhi NCR.

Machine Learning with R has been designed for the provision of having strong hold in creating Machine learning algorithms with the base of R. This has been preferred as the best and robust platform for having Machine Learning systems.

Why To Pursue Machine Learning?

  • Machine learning is almost acquiring the globe in terms of resource requirement for tech companies, who are into the same domain and execute the projects on Machine Learning.
  • There is a tentative estimation for machine learning market expansion from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022.

Objective

  • To achieve fundamental understanding of what is Machine Learning
  • To understand how is Machine Learning making profound impact in making smart business decisions
  • To gain knowledge of various Machine Learning algorithms, understanding problems and applying practical solutions
  • To get started with Machine Learning

Who Should Attend

  • Professionals working in various industries who want to formally understand fundamentals of Machine Learning
  • Enthusiasts who want to keep pace with the changing technologies

Prerequisites

  • Basic knowledge of Cloud platform
  • Basic mathematics
  • Basic programming knowledge: Not mandatory
  • Learning acumen
  • Under-grad or Post-grad in Maths/ Stats/ Economics/ Commerce/ Finance
  • MBA Students (All Streams)
  • 3rd and Final Year B.E./ B.Tech
  • Banking/ Finance/ IT/ KPO Working Professional
  • Software professionals with ETL/Programming background

Machine Learning using R Training Syllabus
Total Duration: 42:00:00 hrs

Module 1- Introduction to Data Analytics
Duration: 04:00:00 hrs

 

Objectives:

  • This module introduces you to some of the important keywords in R like Business Intelligence, Business Analytics, Data and Information.
  • You can also learn how R can play an important role in solving complex analytical problems.
  • This module tells you what is R and how it is used by the giants like Google, Facebook, etc.
  • Also, you will learn use of ‘R’ in the industry, this module also helps you compare R with other software in analytics, install R and its packages.

Topics:

  • Business Analytics, Data, Information
  • Understanding Business Analytics and R
  • Compare R with other software in analytics
  • Install R
  • Perform basic operations in R using command line
  • Learn the use of IDE R Studio
  • Use the ‘R help’ feature in R

Module 2- Introduction to R programming
Duration: 03:00:00 hrs

 

Objectives:

  • This module starts from the basics of R programming like data types and functions.
  • In this module, we present a scenario and let you think about the options to resolve it, such as which data type should one to store the variable or which R function that can help you in this scenario.
  • You will also learn how to apply the ‘join’ function in SQL.

Topics

  • Variables in R
  • Scalars
  • Vectors
  • Matrices
  • List
  • Data frames
  • Using c, Cbind, Rbind, attach and detach functions in R
  • Factors

 

Module 3- Data Manipulation in R
Duration: 04:00:00 hrs

Objectives:

  • In this module, we start with a sample of a dirty data set and perform Data Cleaning on it, resulting in a data set, which is ready for any analysis.
  • Thus using and exploring the popular functions required to clean data in R.

Topics

  • Data sorting
  • Find and remove duplicates record
  • Cleaning data
  • Recoding data
  • Merging data
  • Slicing of Data
  • Merging Data
  • Apply functions

Module 4- Data Import techniques in R
Duration: 04:00:00 hrs

Objectives:

  • This module tells you about the versatility and robustness of R which can take-up data in a variety of formats, be it from a csv file to the data scraped from a website.
  • This module teaches you various data importing techniques in R.

Topics

  • Reading Data
  • Writing Data
  • Basic SQL queries in R
  • Web Scraping

Module 5- Exploratory Data Analysis
Duration: 05:00:00 hrs

Objectives:

  • In this module, you will learn that exploratory data analysis is an important step in the analysis.
  • EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis. You will also learn about the various tasks involved in a typical EDA process.

Topics

  • Box plot
  • Histogram
  • Pareto charts
  • Pie graph
  • Line chart
  • Scatterplot
  • Developing Graphs

Module 6- Basics of Statistics & Linear & Logistic Regression
Duration: 05:00:00 hrs

Objectives:

  • This module touches the base of Descriptive and Inferential Statistics and Probabilities & ‘Regression Techniques’.
  • Linear and logistic regression is explained from the basics with the examples and it is implemented in R using two case studies dedicated to each type of Regression discussed.

Topics

  • Basics of Statistics
  • Inferencial statistics
  • Probability
  • Hypothesis
  • Standard deviation
  • Outliers
  • Correlation
  • Linear & Logistic Regression

Module 7- Data Mining: Clustering techniques, Regression & Classification
Duratio : 04:00:00 hrs

Objectives:

  • Linear and logistic regression is explained from the basics with the examples and it is implemented in R using two case studies dedicated to each type of Regression discussed.
  • The two Machine Learning types are Supervised Learning and Unsupervised Learning and the difference between the two types.
  • We will also discuss the process involved in ‘K-means Clustering’, the various statistical measures you need to know to implement it in this module.

Topics

  • Introduction to Data Mining
  • Understanding Machine Learning
  • Supervised and Unsupervised Machine Learning Algorithms
  • K- means clustering

Module 8- Anova & Sentiment Analysis
Duration : 02:00:00 hrs

Objectives:

  • This module tells you about the Analysis of Variance (Anova) Technique.
  • The algorithm and various aspects of Anova have been discussed in this module
  • Additionally, this module also deals with Sentiment Analysis and how we can fetch, extract and mine live data from Twitter to find out the sentiment of the tweets.

Topics

  • Anova
  • Sentiment Analysis

Module 9- Data Mining:  Decision Trees and Random Forest
Duration: 03:00:00 hrs

Objectives:

  • This module covers the concepts of Decision Trees and Random Forest.
  • The algorithm of Random Forests is discussed in a step-wise approach and explained with real-life examples.

Topics

  • Decision Tree
  • Concepts of Random Forest
  • Working of Random Forest
  • Features of Random Forest

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Fees

 

Application Fees INR 0
Total Program Fees INR 30,000
The Program Fees can also be paid in installments
Registration  Fees INR 5000 (Non-Refundable)
First installment of the Program Fees

(Needs to Paid One Week Before the Batch Start Date)

INR 12,000
Second installment of the Program Fees

(Needs To Be Paid in the 6th Class)

INR 13,000
Discount on Program Fees: 10% Early Bird Discount valid till 16/03/19

10%  Extra discount on Lumsum Payment

 

Batch Closed:             17th Mar 2019

New Batch Starting:  30th March 2019 (Few Seats Left)

2nd Batch:                   07th April 2019

Admission Open Till: 29th March 2019 ( If Required Seats Filled, the admissions will be closed even before the Closing Date)

 

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