DATA ANALYTICS LAB
Pioneering the future of decision-making with advanced data analysis, optimization techniques, and statistical modeling.
EXPLORE OUR METHODSOUR EXPERT TEAM
INCHARGE
Chandrasekar Raja
LAB STAFF
Ramya Sharma
Nathiya Murali
Arunambigai Ramesh
Poonkodi Sathiyamoorthy
A Tamilarasan
OUR ANALYSIS METHODOLOGIES
MULTI-CRITERIA DECISION MAKING (MCDM)
Weight Allocation Methods
Weight allocation methods are essential in multi-criteria decision-making (MCDM) to assign significance to criteria. These methods support robust and rational decision-making by determining the relative importance of each criterion in complex analytical ecosystems.
Additional MCDM Methods
Additional MCDM methods enhance decision-making by evaluating alternatives based on multiple criteria. These approaches provide diverse analytical strategies to support nuanced and context-sensitive evaluations in complex scenarios.
STATISTICAL ANALYSIS (SPSS)
Descriptive Statistics
Descriptive statistics summarise and organise data through measures such as mean, median, mode, variance, and standard deviation. These tools are fundamental for understanding the distribution, central tendency, and variability within datasets.
Inferential Statistics
Inferential statistics draw conclusions about a population based on sample data using techniques like hypothesis testing, confidence intervals, and regression analysis. They help make predictions, determine relationships, and assess statistical significance, allowing generalisations beyond the observed dataset with a quantifiable degree of uncertainty.
Non-Parametric Tests
Non-parametric tests analyse data without assuming a specific distribution, making them suitable for small samples and ordinal or skewed data.
Factor & Reliability Analysis
Factor analysis identifies underlying relationships between variables by grouping them into latent factors, reducing dimensionality while preserving key information. Reliability analysis assesses the consistency of a measurement scale, often using Cronbach’s alpha, ensuring the instrument’s dependability in capturing true constructs without excessive random error.
Multivariate Analysis
Multivariate analysis examines multiple variables simultaneously to uncover patterns, relationships, and dependencies. It is widely used in fields like finance, marketing, and social sciences for complex decision-making.
Time Series & Forecasting
Time series analysis examines data points collected sequentially over time to identify trends, seasonal patterns, and cyclic behaviours. Forecasting uses models like ARIMA, exponential smoothing, and machine learning techniques to predict future values, aiding decision-making in finance, economics, and supply chain management.
Data Reduction & Transformation
Data reduction simplifies datasets by eliminating redundancy while preserving essential information, using techniques like PCA, factor analysis, and feature selection. Data transformation converts data into a suitable format through normalisation, standardisation, or logarithmic scaling, enhancing interpretability, model performance, and ensuring consistency in statistical analysis.
Structural Equation Model
Structural Equation Modelling (SEM) is a multivariate technique that analyses complex relationships among observed and latent variables. Combining factor analysis and regression, SEM evaluates causal relationships using path diagrams, fit indices, and estimation methods like Maximum Likelihood, widely applied in psychology, economics, and social sciences.
MACHINE LEARNING ALGORITHMS
Optimisation Algorithms (Jupyter Notebook)
Optimisation algorithms are effective methods for solving optimisation problems, aiming to determine the most efficient solution within given constraints. These algorithms are widely utilised across diverse fields, including engineering and operations research, to enhance efficiency and decision-making by systematically evaluating possible solutions.
OUR LABORATORY
