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.