Data Science with R-Programming

Data Science with R Training

Data Science with R Programming
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* Course Id : DSCI-RPRG
* Duration : 30 Hours

Overview
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* The course has the most important tools of data science and R programming language
* R, An open source programming language, is popularly used among statisticians, data miners, data analysts, etc
* Using R programming the Data Science is obtaining big data analysis packages, documentation and open source due to flexibility

Prerequisites
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* The participants should have the basic knowledge of statistics and computer programming

Objective
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* Understand the basic knowledge of Data Science with R Language
* Understand and Able to analyze the Built-in R Dataset
* Implement and Able to work on Statistics models using R

Course Structure
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* We provide more focus on hands-on in our technical courses (typically 80% hands-on/20% theory)
* Students get the capability to apply the material they learn to real-world problems

Materials Provided
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* PDF of slides and hands-on exercises
* Access to instance with lab environment

Software Requirements
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* Any current internet browser

Hardware Requirements
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* Processor: 1.2 GHz
* RAM: 512 MB
* Disk space: 1 GB
* Network Connection with low latency (<250ms) to Internet

Course Outline
Unitwise 
Data Science with R Course Outline

##Unit 1
* Introduction to Data Science Methodologies
* Correlation / AssociationRegressionCategorical variables
* Data Preparation
##Unit 2
* Logistic Regression
* Cluster AnalysisClassification Models
* Introduction and to Forecasting Techniques
##Unit 3
* Advanced Time Series Modeling
* Stock market prediction
* Pharmaceuticals
* Market Research
##Unit 4
* Machine Learning
* Fraud Analytics
* Text Analytics
* Social Media Analytics

Detailed Data Science with R Programming course outline
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* 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 built-in R dataset or UCI ML repository dataset

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

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

 
 
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