Data Science with R Course in Dubai

Data Science with R course is designed for software programmers, statisticians and data miners who are looking forward for developing statistical software using R programming. If you are trying to understand the R programming language as a beginner, this tutorial will give you enough understanding on almost all the concepts of the language from where you can take yourself to higher levels of expertise.

Data Science with R Course Overview

Before proceeding with Data Science with R course, you should have a basic understanding of Computer Programming terminologies. A basic understanding of any of the programming languages will help you in understanding the R programming concepts and move fast on the learning track.

What you will Learn in Data science With R Course?

  • Understand critical programming language concepts.
  • Make use of R loop functions and debugging tools
  • Configure statistical programming software
  • Collect detailed information using R profiler

Data Science With R Course Syllabus:

Beginners For Data Science With R Course Syllabus:

  • Introduction to R Programming
  • R Installation & Setting R Environment
  • Variables, Operators & Data types
  • Structures
  • Vectors
  • Vector Manipulation & Sub-Setting
  • Constants
  • R Studio 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
  • DPlyr Package Part 2
  • 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, Violin Plot, 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

Benefits of Data Science With R Course:

  • R language is Open Source.
  • R language is an all-in-one package of a Statistical Analysis toolkit
  • R has excellent charting benefits
  • R language has robust and vibrant online community
  • R language has a powerful package ecosystem
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    • To communicate effectively
    • To face for the interviewsย 
    • To find a better job opportunity
    • To speak with different nationalities
    • We engage the students in more practical sessions with our certified trainers.
    • Online and offline
    • We have Cambridge certified trainers with corporate level experienced.
    • You will get more practical knowledge than theory.
    • You will be trained by experts.
    • You will get both institutional certificate and the KHDA certificate.
    • Spoton provides you the facility to schedule the classes according to your availability.
    • You will get the study materials to study further.
    • This course is about how we use the English theoretical knowledge into practice.
    • The Course is designed approximately one and half months.
    • The duration can be adjustable according to your needs.
    • If you need to complete the course as soon as possible we can provide you for fast track.
    • It will depend on the studentโ€™s and the trainerโ€™s availability.
    • You can select any three days including both weekdays and weekends.
    • Timing will be flexible ( 2 hours per day)
    • It is open from 9.30am-9.00pm

    We offer two certificate:

    • Institutional certificate
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