Data Analytics Laboratory



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Our Data Analytics lab is well-equipped with advanced data analysis software like SAS, SPSS, MINITAB, MATLAB, Mathematica, iThink, R, Python, and CPLEX. Our research on data analytics focuses on the following areas:

  • i)Safety analytics
  • ii)Health analytics
  • iii) Quality analytics
  • iv)Business analytics


The methodologies, mathematics, techniques and algorithms needed for our research are drawn from statistics (both frequentist and Bayesian approaches), machine learning, data mining, and operations research. We continually adopt advanced technologies such as information & communication technology (ICT), internet of things (IoT), and analytics infrastructure for data capture and storing. Our primary focus is on learning from data, predict the future, make real-time analysis and make automated data driven decision making. We use among others the following techniques -

Statistics-based techniques

  • • ANOVA and MANOVA
  • • Regression (both linear and non-linear)
  • • Generalized linear models (e.g., logistic regression, Poisson regression, etc.)
  • • Discriminant analysis
  • • Factor analysis
  • • Principle component analysis
  • • Structural equation modeling
  • • Multi-block partial least squares (MBPLS)
  • • Canonical correlation analysis
  • • Conjoint analysis
  • • Correspondence analysis
  • • Multidimensional scaling

Data mining and machine learning-based techniques

  • Supervised learning ---
    •  
    1. Classification and regression
    2. Decision tree
    3. Instance-based learning (like k-NN, SVM)
     
  • Unsupervised learning ---
    1. Clustering
    2. Association rule mining

  • Semi-supervised learning

  • Reinforced learning

Operations research-based techniques

  • Linear and non-linear programming
  • Queuing theory
  • Game theory
  • Stochastic processes (e.g., Markov process)
  • Simulation

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