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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
  • 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

Data Preparation

  • Data preparation & Variable identification
  • Advanced regression
  • Parameter Estimation / Interpretation
  • Robust Regression
  • Accuracy in Parameter Estimation

Logistic Regression

  • Introduction to Logistic Regression
  • Logit Function
  • Training-Validation approach
  • Lift charts
  • Decile Analysis

  • 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

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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