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Certificate program in business analytics

Program Highlights:
Big Data 101,R Programming,Statistics 101,Statistics with R,Data Visualization with Tableau

Certificate program in business analytics

This Program on Business Analytics – designed by veterans in the Analytics industry; helps to establish a decent career in the growing Data and Analytics domain. This uniquely blended Program is brought to you by Praxis, a Top-ranked Analytics B-School in India.

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INR 14,000

Program Summary

  • 9 credits
    Credits

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

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  • Duration 3 months
  • 40 Hours of projects/Assignments
  • 98 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

    Data Visualization with Tableau

    • Need for visualizing data

      • Same dataset, different interpretation
      • Read texts well, not numbers
      • Brain processes visuals by short circuiting brain’s pathways
      • Quicker conclusions | Speed
    • Research methodologies

      • Problem Formulation
      • Literature review
      • Methodology
      • Analysis
      • Finding and Interpretation
      • Suggestion
      • Conclusion
      • Bibliography
    • Importance of Big data visualization

      • Traditional Visualization
      • Big Data Visualization
    • Tableau product offerings

    • Installation of Tableau Public

    • Working with Tableau - Live Case study/Discussion

    • Creating interactive dashboards with Tableau Public

    • Case study discussion

      • HR – Case Study with Data
    • Story Boarding with Tableau Public

    • Case study discussion

    • Geomapping in Tableau

      • Create a geographic hierarchy
      • Build a basic map
      • Change from points to polygons
      • Add visual detail
      • Add labels
      • Customize your background map
    • Qlik view – Basics

      • Download QlikView Personal Edition and Install
    • Google charts – Basics

      • Creating a simple Google Chart with in data
      • Image generation, Line, bar, and pie charts.
      • Scatter plot
      • Google-o-meter
      • Map,Radar,Venn diagram
      • Specification of attributes
    • Dynamic charts with Google Docs

      • Using Google Docs as database to store graphical data
      • Specifying the range of data and selecting columns
      • Creating an interactive Google Chart with Google Docs data
    • Supplementary material & Case study discussion

    • Closing session & Queries

  • 5

    Statistics with R

    • Introduction to Data

      • Data Basics
      • Overview of data collection principles
      • Experiments - Principles of experiment design
      • Examining Numerical and Categorical data
      • Comparing numerical data across groups
    • Introduction to Probability

      • Introduction
      • Conditional probability
      • Bayes’ Rule
    • Distributions

      • Discrete Distributions
      • Continuous Distributions
    • Introduction to linear regression

      • Correlation
      • Line fitting
      • Fitted values
      • Residuals
      • Basic introduction to multiple regression
    • Foundations for inference and estimation

      • Variability in estimates
      • Sampling distribution
      • Confidence intervals
      • Margin of error and ascertaining a sample size
    • Foundations for inference and hypothesis testing

      • Nearly normal population with known SD
      • Hypothesis testing framework
      • Two Tailed and One Tailed tests
      • Testing hypothesis using confidence intervals and Critical Z values
      • One-sample means with the t distribution with unknown population SD
      • Inference for a single proportion
      • Decision errors (Type 1 and 2)
      • Hypothesis testing using p-values
      • Choosing a significance level
      • Power and the type 2 error rate
    • Linear Regression and Multiple Regression

      • Introduction to F-statistic
      • Hypothesis Tests
      • Intervals
      • Coefficient of Multiple Determination
      • Interpreting the model output

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