DRAT Syllabus

Computer science fundamentals covering mathematical foundations of computing, such as linear algebra (vector space, inner product space, normed vector space, eigenvalues, eigenvectors, systems of linear equations and solutions, LU and singular value decomposition), probability and statistics (Bayes’ theorem, probability distributions, hypothesis testing), and optimisation techniques (gradient descent, constrained and unconstrained optimisation). Programming, data structures, and algorithms include Python programming, basic data structures, searching and sorting, and graph algorithms. Database management covers the ER model, the relational model, SQL, integrity constraints, indexing, data transformation, including normalisation, sampling, and compression. The machine learning section includes supervised learning (regression, classification, SVM, decision trees, random forests, ensemble methods), unsupervised learning (clustering, dimensionality reduction using PCA and LDA), and model evaluation metrics. Deep learning covers neural networks (perceptron, MLP, backpropagation), optimization, regularization, convolutional neural networks (CNN), recurrent neural networks (RNN), LSTM, GRU, transformers, and large language models (LLMs).

Computer science fundamentals covering mathematical foundations of computing such as linear algebra (vector space, matrices, inner product space, normed vector space, eigenvalues, eigenvectors, systems of linear equations and solutions, LU and singular value decomposition), probability and statistics (Bayes’ theorem, probability distributions, hypothesis testing), and optimisation techniques (gradient descent, constrained and unconstrained optimisation). Programming, data structures, and algorithms include Python programming, basic data structures, searching and sorting, and graph algorithms. Analysis of algorithms (algorithm efficiency, design techniques, computational complexity), computer organization and architecture (computer structure, instruction execution, memory hierarchy, I/O interface), theory of computation (automata, formal languages, Turing machines, computational complexity), operating systems (processes and threads, memory management, file systems, concurrency, system security), and computer networks and security (network protocols, addressing, routing, transport mechanisms, cryptography, security).

Management concepts and functions; organisational behaviour elements such as personality, perception, motivation, leadership, group dynamics, communication, organisational culture, change management, and stress management. HR planning, industrial relations, employee engagement, strategic HRM, HR analytics, business ethics and corporate governance, statistics for management, operations research, strategic management, entrepreneurship development, marketing management, and operations management.

Linear Algebra: vector space, eigenvalues, trace, determinants, singular value decomposition. Calculus: limits, differentiation, definite integration, L’Hopital’s rule, discontinuity. Differential Equations: slope fields, separation of variables, existence and uniqueness of solutions. Numerical Methods: root-finding methods, Euler method for ODE, RK4 method for ODEs, gradient descent, computer basics and programming.

Organic Chemistry: reaction mechanisms, name reactions, stereochemistry, aromaticity, pericyclic reactions, photochemistry, retrosynthesis, spectroscopy. Medicinal Chemistry and Drug Discovery: drug design and development, structure-activity relationship (SAR), pharmacokinetics (ADME/T) and pharmacodynamics, target identification, mechanisms of drug action, virtual screening, pharmacophore modelling, ADMET prediction. Computational Chemistry: molecular mechanics, quantum chemistry, density functional theory (DFT), and molecular dynamics. Machine Learning and AI in Chemistry: supervised and unsupervised learning, regression, classification, clustering, neural networks and deep learning, QSAR/QSPR modelling, graph convolutional networks (GCN), transfer learning, model interpretability.

GIS and remote sensing fundamentals, spatial data models (vector and raster), geospatial data processing, spatial interpolation, and visualisation. Probability and statistics for spatial data, spatial autocorrelation, spatial regression, and spatial point pattern analysis. Machine Learning and Deep Learning applications in geospatial sciences: spatio-temporal prediction, time series analysis, spatio-temporal data modelling, change detection, and big data analytics. Land-use and land-cover change prediction, soil and crop monitoring, climate and hydrological modelling, urban growth, infrastructure planning, and disaster risk assessment.

Digital Signal Processing (DSP), Digital Image Processing (DIP), Fundamentals of Quantum Image Processing (QIP), Synthetic Aperture Radar (SAR) Image Acquisition and Processing.

Concepts of sustainability, environmental, social, and economic dimensions, global frameworks for sustainability, social-ecological systems and coupled human-natural systems, thresholds, climate change, tipping points, and regime shifts, environmental risk and impact assessment, environmental management, sustainable resource management, circular economy and sustainable consumption, nature-based solutions, climate adaptation and mitigation, net zero pathways, ecosystem restoration, innovation for sustainable development, sustainability indicators and metrics, and sustainability analytics.

Eco-physiology: phenological studies, plant-environment interactions, phenological monitoring using both field observations and remote sensing techniques, floral radiometry, and colour science.

Classification of materials including metals, ceramics, polymers, and composites; mechanical properties such as stress-strain response, elastic, anelastic, and plastic deformation at room temperature; electronic properties including free electron theory, Fermi energy, density of states, elements of band theory, semiconductors, Hall effect, dielectric, piezoelectric, and ferroelectric behavior; magnetic properties including origin of magnetism, paramagnetism, diamagnetism, ferromagnetism, ferrimagnetism; thermal properties such as specific heat, thermal conduction, thermal diffusivity, thermal expansion, thermoelectric effects; optical properties including refractive index, absorption, transmission of electromagnetic radiation, with examples of materials and their applications; and electronic devices including energy bands in semiconductors, carrier transport by diffusion and drift, mobility and resistivity, generation and recombination of carriers, Poisson and continuity equations, PN junctions, Zener diodes, BJTs, MOS capacitors, MOSFETs, LEDs, photodiodes, and solar cells.