Data Science and R Programming

Become a Expert & Specialization in Data Science and R Programming
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About Data Science and R Programming

R is a programming language and free software environment for statistical computing and graphics supported. R’s open interfaces allow it to integrate with other applications and systems. It is a powerful language used widely for data analysis and statistical computing. Packages such as dplyr, tidyr, readr, data.table, SparkR, ggplot2 have made data manipulation, visualization and computation much faster. To Become a Data Scientist, you must know starting from basics in Statistics, Data Management and Analytics to advanced topics like Machine Learning and Big Data.

Course Outline

  • Why R?
  • Approaches to Machine Learning
  • Data Cleaning
  • Data Integration
  • Installing R Programming Console Software
  • Understanding the Comprehensive R Archive Network
  • Installing RStudio: The IDE
  • Histograms and Density Plots
  • Dot Plots
  • Bar Plots
  • Line Charts
  • Pie Charts
  • Box Plots
  • Scatter Plots
  • Graphical Parameters
  • Axes and Text
  • Combining Plots
  • Lattice Graphs
  • ggplot2 Graphs
  • Probability Plots
  • Mosaic Plots
  • Correlograms
  • Interactive Graphs
  • Read TXT files with read.table()
  • Read CSV files into R
  • read.delim() for Delimited Files
  • XLConnect Package for Reading Excel Files
  • Read RDBMS data
  • Read JSON data
  • Read XML data
  • Read HTML data
  • SPSS data
  • Read Stata data
  • Read Systat data
  • Read SAS data
  • Read Minitab data
  • Read RDA or
  • RData data
  • Introduction to Data Types and Structures
  • Understanding Basic Data Types in R
  • Lists
  • Matrices
  • Introduction to Function Components
  • Writing functions in R
  • Lexical Scoping
  • Function Arguments
  • Special Calls
  • Return Values
  • Objects Attributes
  • Testing and Corecion
  • Combining data frames
  • Special Columns
  • Manipulate and Analyze data
  • Creating the leadership data frame
  • Renaming variables
  • Missing values
  • Data values
  • Type conversions
  • Sorting data
  • Merging datasets
  • Subsetting operators
  • Using SQL statements to manipulate data frames
  • Components Analysis
  • Cluster Analysis
  • Discriminant Analysis
  • Statistical Tests
  • Missing Value Treatment
  • Defining Statistical Models
  • Linear Models
  • Generic Functions for Extracting Model Information
  • Analysis of Variance and Model Comparison
  • Generalized Linear Models
  • Some Non-Standard Models
  • Linear Regression
  • Non-Linear Regression (NLS)
  • Logistic Regression
  • Isotonic Regression
  • Decision Tree
  • Support Vector Machines (SVM)
  • Naive Bayes
  • K-Nearest Neighbours (kNN)
  • K-Means
  • Streaming K-Means
  • Gaussian Mixture
  • Random Forest
  • Dimensionality Reduction Algorithms

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