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Course ID: 43206
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InnovGene Solutions provides course of Data Analytics Using R-Language for fresh graduates, employees looking for career change, IT professional to upgrade their skills in Data analytics. The course is fully customized based on the need of the candidate. A detailed introduction to statistics will be provided.
COURSE SYLLABUS
Module 1: Intro To Data Analysis
Topics: Basic Programming, Analytics, Plotting, and Data Handling
Module 2: Introduction To R
Topics: What is R?, Need for R, R user Interfaces, Oracleâ??s Strategy for R, Working with Data in R
Module 3: Introduction To ORE
Topics: Starting R and Loading ORE, Prerequisites for using ORE, Basic Database Interaction with ORE
Module 4: Installing Packages
Topics: Finding and Installing Resources, Visualization with R
Module 5: Data Structures, Variables
Topics: Data Types, Sub Setting, Data Structures, Assignment, Variables, Indexing, Viewing Summaries and Data, Objects, Naming Conventions, Reading Data from Structured Text Files, Built-In Data, Reading Data using ODBC
Module 6: Data Handling
Topics: Importing or Exporting Data to Multiple Formats, Date and Date-Time Classes in R, Handling Data Frames, PLYR Package for Easy Data Manipulation, Formatting Dates for Modeling
Module 7: Control Flow
Topics: Truth Testing, Looping, Vectorized Calculations, Branching, Functions in Depth, Descriptive Statistics, Inferential Statistics, Group by Calculations,
Module 8: Functions
Topics: Writing user Defined Functions, Installing Packages, the â??Applyâ?? Family of Functions, Commonly used Built in Functions, Basic Visualization, Looping Functions
Module 9: Graphics
Topics: Base Graphics System in R, Exporting Graphics to Different Formats, Advanced R Graphics, Dot Plots, Bar Charts, Scatterplots, Histograms, and Whiskers, Learning the Grammar of Graphics, Quick Plot Function, Graphics for Exploratory Data Analysis, Building Graphics by Pieces, Standard Graphic Displays, Axes, Labels, Titles, Legends
Module 10: Statistical Analysis With R
Topics: Linear Models, Advanced Statistical Modeling with R, Survival Analysis, Density Estimation, Classification, Clustering, Generalized Linear Models
Module 11: Regression
Topics: Logistic, Gamma and Poisson Regression, Covariance Structures, Interpreting Random Effects in Models, Random Effects Introduction, Clustered Data, Prediction in Random Effects, Longitudinal Data, Covariance Structures, Marginal Versus Conditional Models
Module 12: Advanced Missing Data Techniques
Topics: Implications for Analysis, Multiple Imputation, AMELIA Package, Study of Different Types of Missing Data.
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