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Advanced Data Science with R

Program Highlights:
Praxis Certification, Placement Support

Advanced Data Science with R

This Short-Term Program – designed by veterans in the Analytics industry; helps you master the ‘R’ tool, which is predominantly used in Data Science. This uniquely blended Program is brought to by Praxis, a Top-ranked Analytics B-School in India.

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INR 12,500

Program Summary

  • 4 credits
    Credits

    With this course, you are 5 credits short of an assured placement.

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  • Duration 3 Months
  • 5 Hours of projects/Assignments
  • 50 Hours of online sessions

Course Topics

  • 1

    Big Data 101

    • Big Data Characteristics

      • Volume
      • Variety
      • Velocity
      • Veracity
      • Valence
      • Value
    • Big Data and Business

    • Data Relationships and Data Model

      • One-to-one relationship
      • One-to-many relationship
      • Many-to-many relationship
      • Flat model
      • Hierarchical model
      • Network model
      • Relational model
      • Star schema model
      • Data vault model
    • Data Grouping

    • Clustering Algorithms

      • partitioning
      • hierarchical
      • grid based
      • density based
      • model based
    • Getting ready for Clustering Algorithms

    • Clustering Algorithms – UPGMA, single Link Clustering

    • KPIs, Businesses & Data Elements

    • Mapping for business outcomes

      • Define the pain point
      • Define the goal
      • Identify the actors
      • Identify the impacts
      • Identify the deliverables
      • Creating your impact map
    • Basic Query

    • Advanced Query – Embedding

    • Introduction to key mathematical concepts

      • eigenvalues and eigenvectors
    • Application of eigenvalues and eigenvectors

      • investigate prototypical problems of ranking big data
    • Application of the graph Laplacian

      • investigate prototypical problems of clustering big data
    • Application of PCA and SVD

      • investigate prototypical problems of big data compression
  • 2

    Statistics 101

    • Introduction to Statistics

    • Introduction to Statistics – II

    • Measures of Central Tendency, Spread and Shape – I

    • Measures of Central Tendency, Spread and Shape – II

    • Measures of Central Tendency, Spread and Shape – III

  • 3

    R Programming

    • R Programming

    • Introduction to R – I

    • Introduction to R – II

    • Common Data Structures in R

    • Conditional Operation and Loops

    • Looping in R using Apply Family Functions

    • Creating User Defined Functions in R

    • Graphics with R

    • Advanced Graphics with R

  • 4

    Machine Learning with R

    • Introduction to R, R Studio and Basic R Operations

    • Import, Export and Data Manipulation with R

    • Concept of data structure in R

    • Concept of Index in R

    • Creating Boolean index in R based on conditions

    • Hands on exercise

    • Understanding Loops in R

    • Building User functions in R

    • Basic R Graphics

    • Understanding data clustering concepts and techniques

    • Running clustering in R with Silhouette distance measure for cluster validity

    • Understanding data classification concepts and techniques using decision trees

    • Model building concepts and techniques

    • Concepts of Association Rule Mining

    • Building association rules and interpretation

Industry Collaboration

Program Mentors

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