Home Data Science R Programming

Data Science with R Training Course

Data Science with R online training course covers foundational data science tools and techniques, including getting, cleaning, and exploring data, programming in R, and conducting reproducible research.

  • 50000
  • 55000
Download Brochure
  • Course Includes
  • Live Class Practical Oriented Training
  • 90 + Hrs Instructor LED Training
  • 45 + Hrs Practical Exercise
  • 25 + Hrs Project Work & Assignment
  • Timely Doubt Resolution
  • Dedicated Student Success Mentor
  • Certification & Job Assistance
  • Free Access to Workshop & Webinar
  • No Cost EMI Option
Report

Have Query ?

What you will learn

  • Explore R data structures and syntaxes
  • Read and write data from a local file to a cloud-hosted database
  • Work with data, get summaries, and transform them to fit your needs
  • Explore R language fundamentals, including basic syntax, variables, and types
  • Create functions and use control flow
  • Learn to program in R at a good level
  • Learn how to build and use matrices in R
  • Understand the Normal distribution
  • Foundational R programming concepts such as data types, vectors arithmetic, and indexing
  • How to perform operations in R including sorting, data wrangling using dplyr, and making plots

Requirements

  • Know the fundamentals of programming. Know the basics of SQL. Familiar with the basic math and statistic concepts

Description

|| About Data Science with R Training Course

The Data Science with R programming certification online training 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. The training on Data Science with R provides the skills required to work with real data sets and provide an opportunity to use data to provide data-driven strategic and tactical recommendations. This training will provide some insights on techniques such as linear and logistic regression, ANOVA, Segmentation, Ensemble models, SVM and machine learning in big data. In addition to technical skills, the program also allows students to build effective leadership and communication skills to advance their career upon graduation.

 

The Data Science with R 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. R programming training builds the proficiency in using R programming language for statistical computing and graphics. R, a language and environment, is gaining popularity in getting insight in complex data. The business analyst and other professionals dealing in large amount of data can derive results using the ready-made functions.

Course Content

Lecture-1 R Fundamentals

·      History of  R

·      Introduction to R

·      The R environment

·      What is Statistical Programming?

·      Why use a command line? 

·      Your first R session

·      Practical Exercise              

Lecture-2 Basics of R

·      Recording your work 

·      Basic features of R

·      Calculating with R 

·      Named storage

·      Functions 

·      Exact or approximate? 

·      R is case-sensitive 

·      Listing the objects in the workspace 

·      Vectors

·      Extracting elements from vectors 

·      Vector arithmetic 

·      Simple patterned vectors 

·      Missing values and other special values

·      Character vectors 

·      Factors 

·      More on extracting elements from vectors 

·      Matrices and arrays 

·      Data frames

·      Dates and times

·      Practical Exercise              

Lecture-3 Import and Export data in R

·      Importing data in to R

·      CSV File

·      Excel File

·      Import data from text table

·      SAS and SPSS datasets

·      Exporting Data from R

·      CSV File

·      Text Table

·      Excel File

·      SAS dataset

·      Practical Exercise              

Lecture-4 Merge / Join

·      Inner Join

·      Left Join

·      Right Join

·      Full Join

·      Anti Join

·      Semi Join

·      Practical Exercise              

Lecture-5 Programming statistical graphics

·      High-level plots

·      Bar charts and dot charts

·      Pie charts

·      Histograms

·      Box plots

·      Scatterplots

·      QQ plots

·      Density Plot

·      Choosing a high-level graphic

·      Low-level graphics functions

·      The plotting region and margins

·      Adding to plots

·      Setting graphical parameters

·      Practical Exercise              

Lecture-6 Programming with R

·      Flow control

·      The for() loop

·      The if() statement

·      The while() loop

·      The repeat loop, and the break and next statements

·      Apply

·      Sapply

·      Lapply

·      Managing complexity through functions • What are functions? 

·      Scope of variables

·      Practical Exercise              

Lecture-7 Data in R

·      Modes and Classes

·      Data Storage in R 

·      Testing for Modes and Classes

·      Structure of  R Objects

·      Conversion of Objects

·      Missing Values 

·      Working with Missing Values

·      Practical Exercise              

Lecture-8 Reading and Writing Data

·      Reading Vectors and Matrices

·      Data Frames: read.table

·      Comma- and Tab-Delimited Input Files

·      Fixed-Width Input Files 

·      Extracting Data from R Objects 

·      Connections 

·      Reading Large Data Files

·      Generating Data

·      Sequences

·      Random Numbers 

·      Permutations 

·      Random Permutations

·      Enumerating All Permutations 

·      Working with Sequences  v Spreadsheets 

·      The RODBC Package on Windows 

·      The gdata Package (All Platforms)

·      Saving and Loading R Data Objects

·      Working with Binary Files 

·      Writing R Objects to Files in ASCII Format 

·      The write Function 

·      The write.table function

·      Reading Data from Other Programs 

·      Practical Exercise              

Lecture-9 Dates

·      as.Date

·      The chron Package 

·      POSIX Classes

·      Working with Dates

·      Time Intervals

·      Time Sequences

·      Current time

·      Present date

·      Practical Exercise              

Lecture-10 Factors

·      Using Factors

·      Numeric Factors  vs.  Manipulating Factors 

·      Creating Factors from Continuous Variables

·      Practical Exercise              

Lecture-11 Subscripting

·      Basics of Subscripting 

·      Numeric Subscripts 

·      Character Subscripts 

·      Logical Subscripts

·      Subscripting Matrices and Arrays

·      Specialized Functions for Matrices 

·      Lists

·      Subscripting Data Frames

·      Practical Exercise              

Lecture-12 Character Manipulation

·      Basics of Character Data

·      Displaying and Concatenating Character 

·      Working with Parts of Character Values

·      Regular Expressions in R

·      Basics of Regular Expressions

·      Breaking Apart Character Values

·      Using Regular Expressions in R

·      Substitutions and Tagging

·      Practical Exercise              

Lecture-13 Reshaping Data

·      Modifying Data Frame Variables 

·      Recoding Variables 

·      The recode Function

·      Reshaping Data Frames 

·      The reshape Package

·      Combining Data Frames

·      Practical Exercise              

Lecture-14 Data Manipulation

·      Random Selection of rows and columns

·      Summarization

·      Sort, Arrange

·      Group by

·      Filter

·      Practical Exercise              

Lecture-15 Missing Value and Outlier

·      Identify Missing values

·      Impute missing values

·      Identify Outliers

·      Capping outliers

·      Practical Exercise              

Lecture-16 Introduction to Statistics:

·      Types of Statistics

·      Types of Data

·      Practical Exercise              

Lecture-17 Descriptive Statistics

·      Measures of Central Tendency

·      Measures of Central Tendency – Usage Chart

·      Measures of Dispersion / Variability

·      Measures of Shape

·      Application of Variance/Std Deviation

·      Practical Exercise              

Lecture-18 Hypothesis Testing

·      Applications of Hypothesis Testing (Called T Test or Z Test)

·      Steps in Hypothesis Testing

·      Practical Exercise              

Lecture-19 Anova (Analysis of Variance)

·      What is Anova

·      Anova Steps

·      Simple One-Way Anova

·      Simple Two-Way Anova With Multiple Variables

·      Practical Exercise              

Lecture-20 Chi Square Tests

·      What is Chi-Square

·      Applications of Chi-Square

·      Practical Exercise              
Lecture-21 Correlation

·      Types of Correlation

·      Properties of Correlation

·      Methods of Calculating Correlation

·      Steps to Calculate Correlation

·      Practical Exercise              

Lecture-22 Regression Analysis

·      What is Regression

·      Types of Regression Analysis

·      Properties of The Regression Line

·      Validating the Model

·      Regression Assumptions

·      Data Transformation for Regression

·      Practical Exercise              

Lecture-23 Variable Selection Procedure for Regression

·      Forward Selection Procedure

·      Backward Elimination Procedure

·      Stepwise Regression Method

·      Dummy Variable Analysis

·      Practical Exercise              

Lecture-24 Logistic Regression

·      Likelihood Profiling

·      Assumption

·      Variable Selection Method :- Woe And Iv

·      Model Validation

·      Model Performance

·      Prediction

·      Practical Exercise              

Lecture-25 Cluster Analysis

·      What is cluster

·      Application of clustering

·      Types of clustering

·      K Means

·      Dendrogram

·      Validation of Cluster

·      Practical Exercise              

Lecture-26 Decision Tree

·      What is decision Tree

·      How decision tree works

·      Cart

·      Pruning

·      Overfitting

·      Underfitting

·      Model validation

·      Model performance

·      Practical Exercise              

Lecture-27 Market Basket Analysis

·      What is MBA

·      Application of MBA

·      Support

·      Confidence

·      Lift

·      Rules

·      Practical Exercise              

Lecture-28 Random Forest

·      What is random forest

·      Application of random forest

·      Tune parameters

·      How to tune parameters

·      Model validation

·      Model performance

·      Practical Exercise              

Lecture-29 Support Vector Machine

·      What is support vector machine

·      Why to use SVM

·      Hyperplane

·      Kernel

·      Cost

·      Gamma

·      Model validation

·      Model performance

·      Practical Exercise              

Lecture-30 Naïve bayes

·      What is Naïve bayes

·      Bayes theorem

·      Conditional probability

·      Prior probability

·      Posterior probability

·      Application of Naïve bayes

·      Model validation

·      Model performance

·      Practical Exercise              

Lecture-31 ARIMA

·      What is time series

·      What is Arima

·      Stationary

·      Seasonality

·      Trend

·      How to find p,d,q

·      What are p,d,q

·      Find best model

·      Forecasting

·      Practical Exercise              

Lecture-32 Principal Component Analysis

·         Types of unsupervised learning,

·         Types of clustering

·         Introduction to k-means clustering

·         Math behind k-means

·         Dimensionality reduction with PCA
Lecture-33 Natural Language Processing and Text Mining

·         Introduction to Natural Language Processing (NLP)

·         Introduction to text mining

·         Importance and applications of text mining

·         How NPL works with text mining

·         Writing and reading to word files

·         Language Toolkit (NLTK) environment

·         Text mining: Its cleaning, pre-processing, and text classification

·         Text mining use cases

·         Understanding and manipulating the text with ‘tm’ and ‘stringr’

·         Text mining algorithms

·         The quantification of the text

 

·         TF-IDF and after TF-IDF

 
Lecture-34 Introduction to Deep Learning

·         Introduction to Deep Learning with neural networks

·         Biological neural networks vs artificial neural networks

·         Understanding perception learning algorithm,

·         Introduction to Deep Learning frameworks,

·         Tensorflow constants, variables, and place-holders
Case Studies

Fees

Offline Training @ Vadodara

  • Classroom Based Training
  • Practical Based Training
  • No Cost EMI Option
60000 55000

Online Training preferred

  • Live Virtual Classroom Training
  • 1:1 Doubt Resolution Sessions
  • Recorded Live Lectures*
  • Flexible Schedule
55000 50000

Corporate Training

  • Customized Learning
  • Onsite Based Corporate Training
  • Online Corporate Training
  • Certified Corporate Training

Certification

  • Upon the completion of the Classroom training, you will have an Offline exam that will help you prepare for the Professional certification exam and score top marks. The BIT Certification is awarded upon successfully completing an offline exam after reviewed by experts
  • Upon the completion of the training, you will have an online exam that will help you prepare for the Professional certification exam and score top marks. BIT Certification is awarded upon successfully completing an online exam after reviewed by experts.

Related Courses

Copyright Bitonlinelearn © 2021. All Rights Reserved | Design and Developed By BITINFOTECH

Your experience on this site will be improved by allowing cookies.