Data Science with R Language Training

Data Science with R Language Training Overview

This Data Science with R Programming language training is provided by the real-time expert with a number of real-time use cases. R Programming language is Open source technology which can available to everyone in the market. R Language is most powerful tool consists the features of simulation, graphics, and big data analysis etc together.With the combination of R Programming language and Data Science has become the statistical modeling, more flexible. By using R Language the Data Science is gaining on big data analysis packages, documentation and open source due to flexibility.

Objectives of the Course

• In-depth coverage and Knowledge of Data Science with R Language.
• Understand and Able to analyze the Big Data.
• Understand and able to work on Statistics and Data Mining.
• Able to learn how to use the tools like the tableau, map reduce.

Pre-requisites of the Course

• The learner should have the basic knowledge of statistics and computer programming.
• Preferably a reasonable level of proficiency in any of data handling tool like MS Excel.

Who can attend this course

• Any student or Professional who is looking to build their career in Development or Data Scientist
• All Graduates Can Learn this course

Data Science with R Language Course Content

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

Social Media Analytics

• Social Media Analytics
• Sample text from online