Introduction to Data Analytics

Yesteryears’ trend suggests that the analysis of data have been more in improving operational processes like manufacturing, service provision, quality control and the likes. Literatures of data analysis are also tuned with the orientation to serve the purpose of operational excellence focused towards more of internal processes in the entire business processes. However, every businessman knows how important are the external influences on their processes and available analytical ability hardly succeeded to address the external concerns. Due to this, decisions to tackle external influences are often taken on intuition and perceptions. Many of such decisions often went counterproductive.

Processes more closed to the external concerns are marketing, sales, supply chain, purchase, finance, R&D and in some pockets even HRD. Problems in these areas are that they have regularly huge data and many of them are verbal data and collection of these data are more to serve short-term requirements of accounting, complaint or grievance resolutions, report making etc. Thought process went in to search out whether available data in these areas can give rise to any conclusion to take decisions on the long-term or strategic business planning and this particular phenomenon created to evolve one of the most important disciplines in the modern times, i.e. Data Analytics.

Current practices of developing business analytic to gather business intelligence are more focused towards tabular analysis and data visualisation and less of even basic data analysis and scientific data mining activities requiring good depth of univariate and multivariate statistical techniques. Already known conclusions and wishful conclusions are presented in visual graphs and charts in many of the routinely exercised business analytic reports using quite cost `customised’ softwares! Discover unknown information from the available data, predict future with accuracy, and manage the knowledge of external environment impacting your business is a few key deliverables of Data analytic.

Duration: 4 days

Program Content

  • Introduction to Big Data and Data Analytic
  • How DA is linked to Business
  • Motivation, Examples, Existing trend of Data Analytic, Functional Analytics
  • Need for genuine & Comprehensive outlook
  • 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)
    • Data Dimension reduction (PCA, FA)
    • Examples from Different Functional Analytics
  • 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
    • Surface plot
    • Multivari chart
    • Interval Plot
    • Contour Plot
    • Heat map
    • Stratified Scatter Plot
  • Exploratory Data Analysis
    • Introdution to Summary statistics
    • Probability theory
    • Bayes Therem
    • Some important probability distributions
  • Univariate and Pairwise analysis- Parametric and Non-parametric
  • Discrete and Continuous data analysis
  • Test of Hypothesis
  • Estimation and Confidence Interval
  • Regression and Correlation
    • Simple Linear Regression
    • Multiple Linear Regression
    • Generalised Regression
    • Diagnostics
  • Introduction to Data Mining
  • Supervised Classification
    • Classification rules & misclassification error
    • DA-Linear, Quadratic
    • K-Nearest Neighbour
    • Decision Tree
    • CART
    • Logistic Regression
    • Introduction to ANN & SVM
  • 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 Presiction
  • Benefits from the Program

    • Knowledge and skill gained from the Program will help the participants to lead in the 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

    Who should attend this Program

    • The course is designed for Professionals from the following areas:
      • Manager/Analyst/ Executives who would need to extract information from the data that are routinely encounter from the areas such as Retail, Banking, Finance, BPO, IT, Sales & Marketing, Supply chain, Planning, Strategic management etc. Mathematical background is preferable though not mandatory.