This course will prepare you to: Explain the architecture of the DATASCIENCE WITH R component Configure and use new functionalities in DATASCIENCE WITH R Use the standard DATASCIENCE WITH R Sub Modules. Explain the DATASCIENCE WITH R Controlling Configuration and Customization option.

Data preparation & Variable identification Advanced regression Parameter Estimation / Interpretation Robust Regression Accuracy in Parameter Estimation Using UCI ML repository dataset or Built-in R dataset

Introduction to Logistic Regression Logit Function Training-Validation approach Lift charts Decile Analysis Using UCI ML repository dataset or Built-in R dataset

Case Study with the Data Based on open set data

Supervised Learning Techniques Conceptual Overview Unsupervised Learning Techniques Association Rule Mining Segmentation

Fraud Identification Process in Parts procuring Sample data from online

Text Analytics Sample text from online

Data science With R Training

Sapphire Global DATASCIENCE WITH R Training makes you an expert in using DATASCIENCE WITH R concepts. Enroll now for DATASCIENCE WITH R online training and get through the concepts of data, by utilizing the internal memory for storing a working set. Sapphire Global introduces all the key concepts in DATASCIENCE WITH R to help the learner gain more knowledge. Enroll & Become a DATASCIENCE WITH R Consultant.

Overview

Training Options

INSTRUCTOR LEAD LIVE TRAINING

Live Instructor Online training by Certified & industry expert Trainers
24/7 One Demand Dedicated Server for Hands on Practice.
Flexibility to attend the class at your convenient time.
Earn a Skill Certificate
Professional Resume Preparation end of the trading period.
Plus 45 Days of flexible access e- learning.

CORPORATE TRAINING

Customized Learning delivery model.
Our training practice are perfectly blended with options for effective live instructor lead hands on training.
Training need analysis.
Our Corporate training services are easy accessibility of LMS ( learning Management System ) online or offline – anytime, anywhere, on any of your devices.
24/7 learner assistance and Support

Course Curriculum

Key features

This course will prepare you to:

Explain the architecture of the DATASCIENCE WITH R component
Configure and use new functionalities in DATASCIENCE WITH R
Use the standard DATASCIENCE WITH R Sub Modules.
Explain the DATASCIENCE WITH R Controlling Configuration and Customization option.

Introduction to Data Science Methodologies

Data Types
Introduction to Data Science Tools
Statistics
Approach to Business Problems
Numerical Categorical
R, Python, WEKA, RapidMiner
Hypothesis testing: Z, T, F test Anova, ChiSq

Correlation / AssociationRegressionCategorical variables

Introduction to Correlation Spearman Rank Correlation
OLS Regression – Simple and Multiple Dummy variables
Multiple regression
Assumptions violation – MLE estimates
Using UCI ML repository dataset or Built-in R dataset

Data Preparation

Data preparation & Variable identification
Advanced regression
Parameter Estimation / Interpretation
Robust Regression
Accuracy in Parameter Estimation
Using UCI ML repository dataset or Built-in R dataset

Logistic Regression

Introduction to Logistic Regression
Logit Function
Training-Validation approach
Lift charts
Decile Analysis
Using UCI ML repository dataset or Built-in R dataset

Cluster AnalysisClassification Models

Introduction to Cluster Techniques
Distance Methodologies
Hierarchical and Non-Hierarchical Procedure
K-Means clustering
Introduction to decision trees/segmentation with Case Study
Using UCI ML repository dataset or Built-in R dataset

Introduction and to Forecasting Techniques

Introduction to Time Series
Data and Analysis
Decomposition of Time Series
Trend and Seasonality detection and forecasting
Exponential Smoothing
Building R Dataset
Sales forecasting Case Study

Advanced Time Series Modeling

Box – Jenkins Methodology
Introduction to Auto Regression and Moving Averages, ACF, PACF
Detecting order of ARIMA processes
Seasonal ARIMA Models (P,D,Q)(p,d,q)
Introduction to Multivariate Time-series Analysis
Using built-in R datasets

Stock market prediction

Live example/ live project
Using client given stock prices / taking stock price data

Pharmaceuticals

Case Study with the Data
Based on open set data

Market Research

Case Study with the Data
Based on open set data

Machine Learning

Supervised Learning Techniques
Conceptual Overview
Unsupervised Learning Techniques
Association Rule Mining Segmentation

Fraud Analytics

Fraud Identification Process in Parts procuring
Sample data from online

Text Analytics

Text Analytics
Sample text from online

Practice Test and Interview Questions

Course Advisor

Course Advisor

R. Sreenatha Reddy

SAP FICO / FSCM / TRM / FM / S4HANA Functional Consultant

Professional Experience:

15+ years of Industry experience as Accountant, Consultant, Module Lead, worked on various IT Organizations like IBM/TCS/ITC infotech/SAP Labs across the globe.

Specialized in SAP