Introduction to Data Analytics (IDA)

 

Introduction

Understanding Data and deriving meaningful inferences out of it is essential for growth of any organization. It is therefore important to learn various statistical tools and techniques for data analysis and how they are implemented in exploratory and predictive analytics.

The Program helps participants get a grasp on the subject of Data Analytics and enable them to apply the same in the Organizations.

Duration: 2 days

Program Content

  • Introduction to Data Analytic
    • How DA is linked to Business
    • Motivation, Examples, Existing trend of Data Analytic, Functional Analytics
    • Need for genuine & Comprehensive outlook
    • Introduction to Big Data and R
    • Corevolve Approach of Data Analytic of Seven Steps Analytic Indrastructure
  • Data Preparation
    • Problem formulation
    • Categorise Data
    • T,C,X,Y,Z of Data
    • Meta data
    • Touple Formation
    • Sampling
    • Data Collection, Aggregation and Indexing
    • Data Normalisation
    • Data cleaning (outlier analysis)
    • Introduction to Data Dimension reduction (PCA, FA)
  • Data Visualisation (based on Minitab & R)
    • Frequency Diagram
    • Pareto Chart
    • Histogram
    • Box Plot
    • Dot Plot
    • Stem Leaf Diagram
    • Time Series Plot
    • Scatter plot
    • Matrix plot
    • Stratified Scatter Plot
  • Exploratory Data Analysis
    • Introdution to Summary statistics
    • Probability theory
    • Some important probability distributions
    • Univariate and Pairwise analysis
    • Discrete and Continuous data analysis
    • Estimation and Confidence Interval
    • Correlation and Regression
    • Simple Linear Regression
    • Introduction to Multiple Linear Regression
    • Diagnostics
  • Introduction to Data Mining
  • Supervised Classification
    • Classification rules & misclassification error
    • K-Nearest Neighbour
    • Decision Tree
    • CART
    • Logistic Regression
  • Clustering
    • Lacunas of Supervised classification
    • Concept of Clustering
    • Distance and Similarity Concept
    • Hierarchical clustering
    • K-Means Clustering
  • Affinity Analysis
    • Support, Confidence & Lift
    • Market Basket Analysis
  • Predictive Anaytics
    • Concept of Prediction
    • Prediction diagnostic and error
    • Supervised Classification for Prediction
    • Time Series Analysis for Prediction
  •  

    Benefits from the Program

    • Knowledge and skill gained from the Program will help the participants to contribute significantly to area of Data Analytics in their organization
    • The methodology, tools and techniques learnt can be applied to solve variety of business and technical problems apart from Data Analytics
    • It greatly improves the employability of students pursuing degree courses
    • Provides knowledge and skills to progress towards gaining expertise in this area

     

    Who should attend this Program

    • The course is designed for Professionals from the following areas:
      • Professionals working or desirous of working in Data/Business Analytics area
      • Executives/ Engineers/ Staff with some background in mathematics and from any discipline of any type of industry/ trade.
      • Students pursuing graduate/post graduate in students of Science, Engineering, and Management and Commerce or any other area but with background in Mathematics
      • Entrepreneurs and Management Teams who would like to understand subject of Data Analytics
      • Quality Assurance Engineers, Project Managers, Team leaders, Software Professionals, Practitioners, Software Quality Assurance team members
      • Professionals who are doing research and innovations