Data Analytics With R Course Online Course

Data Analytics with R Course

All levels
English

Course Description

Data Analytics with R training will help you master in R Programming, Data Manipulation, Exploratory Data Analysis, Data Visualization, Data Mining, Regression, Sentiment Analysis and using R Studio for real life case studies on Retail, Social Media.The domain of Data Analytics has been embraced by many industries for the outstanding benefits it offers. Data Analytics is a boon to modern-day businesses. Data Analytics helps businesses in making smarter decisions. Data Analytics improves efficiency and controls risks. Data Analytics also results in cost cuttings. Exploratory data analysis is an approach for summarizing and visualizing the important characteristics of a data set. Data Analytics training will take you through the basics of this powerful language R. From the ground up, you will learn how to develop data for analysis and apply statistical measures to create data visualisations. By exploring the characteristics of data sets, you can analyse and achieve optimum results.

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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 & visualize data & polish your skills in techniques such as Predictive Analytics, Association Rule Mining & much...
Derive meaning from custom created charts which represent complex data, manipulate this data & create statistical models...
Learn to use R, not just as a statistical tool but to create your own functions, objects and packages
Learn where to use algorithms - Decision Trees, Logistic Regression, Support Vector Machines, Ensemble Techniques etc
Perform Analysis of Variance (ANOVA)
Apply various supervised machine learning techniques

Covering Topics

Part 1: Core R Programming

Part 2: Data Manipulation Techniques using R programming

Part 3: Statistical Applications using R programming

Part 4: Case Studies

Curriculum

  Part 1: Core R Programming
Lecture-1 Introduction to Data Analytics and R 
·      Business Intelligence

·      Business Analytics

·      understanding Business Analytics and R

·      History of R

·      The R environment

·      What is Statistical Programming?

·      Practical Exercise              

Lecture-2 Basic features of R 
·      Calculating with R

·      Named storage

·      Functions

·      Exact or approximate?

·      R is case-sensitive

·      Listing the objects in the workspace

·      Vectors

·      Vector arithmetic

·      Missing values and other special values

·      Character vectors

·      Factors

·      Matrices and arrays

·      Data frames

·      Dates and times

·      Practical Exercise              

Lecture-3 Logical vectors and relational operators 
·      Boolean algebra

·      Logical operations in R

·      Relational operators

·      Data input and output

·      Changing directories

·      dump() and source()

·      Redirecting R output

·      Saving and retrieving image files

·      Practical Exercise              

Lecture-4 Programming statistical graphics 
·      High-level plots

·      Choosing a high-level graphic

·      Low-level graphics functions

·      Practical Exercise              

Lecture-5 Programming with R 
·      Flow control

·      Managing complexity through functions

·      Miscellaneous programming tips

·      Some general programming guidelines

·      Debugging and maintenance

·      Efficient programming

·      Practical Exercise              

Lecture-6 Simulation 
·      Monte Carlo simulation

·      Generation of pseudorandom numbers

·      Simulation of other random variables

·      Monte Carlo integration

·      Advanced simulation methods

·      Practical Exercise              

Lecture-7 Computational linear algebra 
·      Vectors and matrices in R

·      Matrix arithmetic

·      Eigenvalues and eigenvectors

·      Practical Exercise              

Lecture-8 Numerical optimization 
·      The golden Section search method

·      Newton–Raphson

·      The Nelder–Mead simplex method

·      Built-in functions

·      Linear programming

·      Practical Exercise

  Part 2: Data Manipulation Techniques using R programming
Lecture-9 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-10 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

·      Working with Sequences

·      Spreadsheets

·      Saving and Loading R Data Objects

·      Working with Binary Files

·      Practical Exercise              

Lecture-11 R and Databases 
·      A Brief Guide to SQL

·      ODBC

·      Using the RODBC Package

·      The DBI Package

·      Accessing a MySQL Database

·      Performing Queries

·      Normalized Tables

·      Getting Data into MySQL

·      More Complex Aggregations

·      Practical Exercise              

Lecture-12 Dates 
·      Date

·      The chron Package

·      POSIX Classes

·      Working with Dates

·      Time Intervals

·      Time Sequences

·      Practical Exercise              

Lecture-13 Factors 
·      Using Factors

·      Numeric Factors

·      Manipulating Factors

·      Creating Factors from Continuous Variables

·      Factors Based on Dates and Times

·      Interactions

·      Practical Exercise              

Lecture-14 Subscripting 
·      Basics of Subscripting

·      Numeric Subscripts

·      Character Subscripts

·      Logical Subscripts

·      Subscripting Matrices and Arrays

·      Practical Exercise              

Lecture-15 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-16 Data Aggregation 
·      Road Map for Aggregation

·      Mapping a Function to a Vector or List

·      Mapping a function to a matrix or array

·      Mapping a Function Based on Groups

·      Practical Exercise              

Lecture-17 Reshaping Data 
·      Modifying Data Frame Variables

·      Recoding Variables

·      The recode Function

·      Reshaping Data Frames

·      Practical Exercise

  Part 3: Statistical Applications using R programming
Lecture-18 Probability and distributions 
·      Random sampling

·      Probability calculations and combinatorics

·      Discrete distributions

·      Continuous distributions

·      The built-in distributions in R

·      Practical Exercise              

Lecture-19 Descriptive statistics and graphics 
·      Summary statistics for a single group

·      Graphical display of distributions

·      Histograms

·      Empirical cumulative distribution

·      Q–Q plots

·      Boxplots

·      Summary statistics by groups

·      Graphics for grouped data

·      Histograms

·      Bar-plots

·      Dot-charts

·      Pie-charts

·      Practical Exercise              

Lecture-20 One- and two-sample tests 
·      One-sample t test

·      Wilcoxon signed-rank test

·      Two-sample t test

·      Comparison of variances

·      Two-sample Wilcoxon test

·      The paired t test

·      Practical Exercise              

Lecture-21 Regression and correlation 
·      Simple linear regression

·      Residuals and fitted values

·      Prediction and confidence bands

·      Correlation

·      Pearson correlation

·      Spearman’s ρ

·      Kendall’s τ

·      Practical Exercise              

Lecture-22 Multiple Regression 
·      Plotting multivariate data

·      Model specification and output

·      Model search

·      Practical Exercise              

Lecture-23 Linear models 
·      Polynomial regression

·      Regression through the origin

·      Design matrices and dummy variables

·      Linearity over groups

·      Interactions

·      Two-way ANOVA with replication

·      Practical Exercise              

Lecture-24 Analysis of variance and the Kruskal–Wallis test 
·      One-way analysis of variance

·      Pairwise comparisons and multiple testing

·      Relaxing the variance assumption

·      Graphical presentation

·      Bartlett’s test

·      Kruskal–Wallis test

·      Two-way analysis of variance

·      Graphics for repeated measurements

·      The Friedman test

·      The ANOVA table in regression analysis

·      Practical Exercise              

Lecture-25 Classification and Tabular data 
·      Single proportions

·      Two independent proportions

·      k proportions, test for trend

·      Practical Exercise              

Lecture-26 Power and the computation of sample size 
·      The principles of power calculations

·      Power of one-sample and paired t tests

·      Power of two-sample t test

·      Practical Exercise              

Lecture-27 Advanced data handling 
·      Recoding variables

·      The cut function

·      Manipulating factor levels

·      Working with dates

·      Text Mining

·      Recoding multiple variables

·      Per-group and per-case procedures

·      Time splitting

·      Practical Exercise              

Lecture-28 Logistic regression 
·      Generalized linear models

·      Logistic regression on tabular data

·      The analysis of deviance table

·      Connection to test for trend

·      Likelihood profiling

·      Presentation as odds-ratio estimates

·      Practical Exercise              

Lecture-29 Survival analysis 
·      Essential concepts

·      Survival objects

·      Kaplan–Meier estimates

·      Practical Exercise              

Lecture-30 Rates and Poisson regression 
·      Basic ideas

·      The Poisson distribution

·      Survival analysis with constant hazard

·      Models with piecewise constant intensities

·      Practical Exercise              

Lecture-31 Nonlinear curve fitting 
·      Basic usage

·      Finding starting values

·      Case Studies

·      Practical Exercise

  Case Studies

Frequently Asked Questions

Basic statistics knowledge.

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.