Data Science With R Training Course Online Course

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Data Science with R Training Course

All levels
English

Course Description

The Data Science with R programming certification 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.

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What you’ll learn

Live Class Practical Oriented Training
Timely Doubt Resolution
Dedicated Student Success Mentor
Certification & Job Assistance
Free Access to Workshop & Webinar
No Cost EMI Option
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
Work with data, get summaries, and transform them to fit your needs
Explore R language fundamentals, including basic syntax, variables, and types
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

Covering Topics

Part 1: Essential to R Programming

Part 2: Data Manipulation Techniques using R Programming

Part 3: Data Science with R

Part 4: Case Studies

Curriculum

  Part 1: Essential to R Programming
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

  Part 2: Data Manipulation Techniques using R Programming
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

  Part 3: Data Science with R

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

Frequently Asked Questions

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

The course offers a variety of online training options, including: Live Virtual Classroom Training: Participate in real-time interactive sessions with instructors and peers. 1:1 Doubt Resolution Sessions: Get personalized assistance and clarification on course-related queries. Recorded Live Lectures*: Access recorded sessions for review or to catch up on missed classes. Flexible Schedule: Enjoy the flexibility to learn at your own pace and according to your schedule.

Live Virtual Classroom Training allows you to attend instructor-led sessions in real-time through an online platform. You can interact with the instructor, ask questions, participate in discussions, and collaborate with fellow learners, simulating the experience of a traditional classroom setting from the comfort of your own space.

If you miss a live session, you can access recorded lectures* to review the content covered during the session. This allows you to catch up on any missed material at your own pace and ensures that you don't fall behind in your learning journey.

The course offers a flexible schedule, allowing you to learn at times that suit you best. Whether you have other commitments or prefer to study during specific hours, the course structure accommodates your needs, enabling you to balance your learning with other responsibilities effectively. *Note: Availability of recorded live lectures may vary depending on the course and training provider.