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Big Data Analytics with R and Hadoop Ecosystem online training

Big Data Analytics with R and Hadoop Ecosystem


Big data analytics is the process of examining large amounts of data of a variety of types to uncover hidden patterns, unknown correlations, and other useful information. Such information can provide competitive advantages over rival organizations and result in business benefits, such as more effective marketing and increased revenue. New methods of working with big data, such as Hadoop and MapReduce, offer alternatives to traditional data warehousing.

Big Data Analytics with R and Hadoop is focused on the techniques of integrating R and Hadoop by various tools such as RHIPE and RHadoop. A powerful data analytics engine can be built, which can process analytics algorithms over a large scale dataset in a scalable manner. This can be implemented through data analytics operations of R, MapReduce, and HDFS of Hadoop.


  • Working professionals, managers and recent graduates are eligible for the program. We do not specify any academic background requirements.
  • Elementary programming skills.


  • It is a 16 days program and extends up to 2hrs each.
  • The format is 40% theory, 80% Hands-on.

  • It is a 4 days program and extends up to 8hrs each.
  • The format is 40% theory, 80% Hands-on.
    Private Classroom arranged on request and minimum attendies for batch is 4.

course content

  • Introduction to Big Data
    • Logistics
    • Analysis through DataVisualization
  • Understanding the "business case" and defining a solution framework
  • An introduction to R programming language and environment
  • Techniques of Pre-processing data (Binning, Normalizing, Filling missing values, removing noise)
  • Data Pre-processing—continued
  • Traps and Errors
    • Confusion matrix, Analyze False positives and False Negatives from a problem perspective
    • Different error measures used in Forecasting
  • Model Selection
    • K-fold validation
  • Introduction to Decision Trees and their structure
  • Construction of Decision Trees through simplified examples
    • Choosing the "best" attribute at each non-leaf node
    • Entropy
    • Information Gain
  • Generalizing Decision Trees
    • Information Content and Gain Ratio
    • Dealing with numerical variables other measures of randomness
  • Inductive learning from a 500-ft view
    • Issues in inductive learning like curse of dimensionality
    • Overfitting
    • Bias-Variance tradeoff
  • Pruning a Decision Tree
    • Cost as a consideration
    • Unwrapping Trees as rules
  • A mathematical model for association analysis
  • Large itemsets and Association Rules
    • Apriori
    • Constructs large itemsets with minisup by iterations
  • Interestingness of discovered association rules
    • Application examples
    • Association analysis vs. Classification
  • Using Association Rules to compare stores
    • Dissociation Rules
    • Sequential Analysis Using
    • Association Rules
  • Data visualization and Story-telling
    • Anatomy of a graph
  • Animated graphs, BI dashboards and the latest trends in data visualization
  • An end-to-end case study in R involving understanding the data
    • Filling the missing values
    • Applying and assessing models and reporting the results.


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