DataScience with R

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Course Overview

Xpertised Offers Advanced and Personalized Instructor Led Online Classroom training on DataScience with R which gives you the opportunity to interact with a DataScience with R instructor and help you enhance yourself to meet the demands of the industry.

Learn from our instructors from the convenience of your home or office. Interact and learn live with trainers and other participants. 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.

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


  • 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

Customer Reviews

Thanks to Xpertised and the tutor who walked me through all the topics with Practical exposure which is helping me in my current project.

Course was quite helpful in terms of understanding of concepts and practicality. Its really a very friendly environment to learn. The timing were mutually chosen, as we both are working professional. I am quite satisfied with the course.


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