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    R Programming Language

    BY
    Simpliv Learning

    Want to learn to develop statistical software using R programming? Join the online programme by Simpliv Learning.

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    Mode

    Online

    Duration

    68 Hours

    Fees

    $ 9 49

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study
    Mode of Delivery Video and Text Based

    Course overview

    R Programming Language course is an online learn-at-your-pace fashion programme, developed by DATAhill Solutions Srinivas Reddy that trains the learners to write statistical software using R programming. The curriculum broadly explores all the major topics of R programming languages such as Variables, Operators & Data types, Structures, VectorsR Installation & Setting R Environment, Constants, Lists Part 2, List Manipulation, Sub-Setting, Matrix Accessing, RStudio Installation & Lists Part 1 and many more.

    R Programming Language online course, offered by Simpliv Learning, demands certain prerequisites to be eligible for the programme. The learner must have a primary understanding of Computer Programming terminologies and basic knowledge of any of the available programming languages that will help them to grasp the concepts of R programming very easily.

    R Programming Language certification is open for all graduates and students who are very keen to know the R programming language from the beginner level content, specifically to software programmers, statisticians and data miners. The online programme also provides the enrolled students with unlimited access to the study materials including the lectures, articles etc. and 20-day money-back guarantee.

    The highlights

    • Online course
    • 20-Day Money-Back Guarantee
    • Learn at your own pace
    • Lifetime Access 
    • Certificate on Completion
    • Access on Android and iOS App

    Program offerings

    • Certificate on completion
    • Access on android and ios app
    • 82 lectures
    • English videos
    • Certification of completion
    • 68+ hours completion time

    Course and certificate fees

    Fees information
    $ 9  $49
    certificate availability

    Yes

    certificate providing authority

    Simpliv Learning

    Who it is for

    Statistician

    What you will learn

    Programming skills R programming

    After the completion of the R Programming Language online certification, the participants will be able to gain a thorough understanding of the R programming language for Statistical Computing and Graphical Representation. Plus, the course will make sure that the learners are able to master the concepts of R  Programming Language such as R Installation & Setting R Environment, Variables, Operators & Data types and whatnot.

    The syllabus

    R Programming Language

    •  Introduction to R Programming
    •  R Installation & Setting R Environment
    •  Variables, Operators & Data types
    •  Structures
    •  Vectors
    •  Vector Manipulation & Sub-Setting
    •  Constants
    •  RStudio Installation & Lists Part 1
    •  Lists Part 2
    •  List Manipulation, Sub-Setting & Merging
    •  List to Vector & Matrix Part 1
    •  Matrix Part 2
    •  Matrix Accessing
    •  Matrix Manipulation, rep function & Data Frame
    •  Data Frame Accessing
    •  Column Bind & Row Bind
    •  Merging Data Frames Part 1
    •  Merging Data Frames Part 2
    •  Melting & Casting
    •  Arrays
    •  Factors
    •  Functions & Control Flow Statements
    •  Strings & String Manipulation with Base Package
    •  String Manipulation with Stringi Package Part 1
    •  String Manipulation with Stringi Package Part 2 & Date and Time Part 1
    •  Date and Time Part 2
    •  Data Extraction from CSV File
    •  Data Extraction from EXCEL File
    •  Data Extraction from CLIPBOARD, URL, XML & JSON Files
    •  Introduction to DBMS
    •  Structured Query Language, MySQL Installation & Normalization
    •  Data Definition Language Commands
    •  Data Manipulation Language Commands
    •  Sub Queries & Constraints
    •  Aggregate Functions, Clauses & Views
    •  Data Extraction from Databases Part 1
    •  Data Extraction from Databases Part 2 & DPlyr Package Part 1
    •  DPlyr Package Part 2
    •  DPlyr Functions on Air Quality Data Set
    •  Plyr Package for Data Analysis
    •  Tidyr Package with Functions
    •  Factor Analysis
    •  Prob.Table & CrossTable
    •  Statistical Observations Part 1
    •  Statistical Observations Part 2
    •  Statistical Analysis on Credit Data set
    •  Data Visualization, Pie Charts, 3D Pie Charts & Bar Charts
    •  Box Plots
    •  Histograms & Line Graphs
    •  Scatter Plots & Scatter plot Matrices
    •  Low-Level Plotting
    •  Bar Plot & Density Plot
    •  Combining Plots
    •  Analysis with Scatter Plot, Box Plot, Histograms, Pie Charts & Basic Plot
    •  Mat Plot, ECDF & Box Plot with IRIS Data set
    •  Additional Box Plot Style Parameters
    •  Set.Seed Function & Preparing Data for Plotting
    •  QPlot, ViolinPlot, Statistical Methods & Correlation Analysis
    •  ChiSquared Test, T-Test, ANOVA, ANCOVA, Time Series Analysis & Survival Anal
    •  Data Exploration and Visualization
    •  Machine Learning, Types of ML with Algorithms
    •  How Machine Solve Real-Time Problems
    •  Nearest Neighbor(KNN) Classification
    •  KNN Classification with Cancer Data set Part 1
    •  KNN Classification with Cancer Data set Part 2
    •  Navie Bayes Classification
    •  Navie Bayes Classification with SMS Spam Data set & Text Mining
    •  WordCloud & Document Term Matrix
    •  Train & Evaluate a Model using Navie Bayes
    •  MarkDown using Knitr Package
    •  Decision Trees
    •  Decision Trees with Credit Data set Part 1
    •  Decision Trees with Credit Data set Part 2
    •  Support Vector Machine, Neural Networks & Random Forest
    •  Regression & Linear Regression
    •  Multiple Regression
    •  Generalized Linear Regression, Non-Linear Regression & Logistic Regression
    •  Clustering
    •  K-Means Clustering with SNS Data Analysis
    •  Association Rules (Market Basket Analysis)
    •  Market Basket Analysis using Association Rules with Groceries Data set
    •  Python Libraries for Data Science

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