GATE DA Visual Syllabus Hub
Master Data Science & AI concepts through interactive, behind-the-scenes visual sandboxes. Free, open, and built for students.
GATE DA Official Syllabus
📊 Probability and Statistics
- Counting (Permutations & Combinations) Live Sandbox
- Probability axioms, Sample space, events, independent events, mutually exclusive events Live Sandbox
- Marginal, conditional and joint probability, Bayes Theorem
- Conditional expectation and variance, mean, median, mode and standard deviation
- Correlation and covariance
- Random variables, discrete random variables and probability mass functions
- Uniform, Bernoulli, binomial distributions
- Continuous random variables and probability distribution functions
- Uniform, exponential, Poisson, normal, standard normal, t-distribution, chi-squared distributions
- Cumulative distribution function, Conditional PDF
- Central limit theorem, confidence intervals, z-test, t-test, chi-squared test
📐 Linear Algebra
- Vector space, subspaces, linear dependence and independence of vectors
- Matrices, projection matrix, orthogonal matrix, idempotent matrix, partition matrix
- Quadratic forms, systems of linear equations and solutions; Gaussian elimination
- Eigenvalues and eigenvectors, determinant, rank, nullity, projections
- LU decomposition, singular value decomposition
📈 Calculus and Optimization
- Functions of a single variable, limit, continuity and differentiability
- Taylor series, maxima and minima, optimization involving a single variable
💻 Programming, Data Structures and Algorithms
- Programming in Python
- Basic data structures: stacks, queues, linked lists, trees, hash tables
- Search algorithms: linear search and binary search
- Basic sorting algorithms: selection sort, bubble sort and insertion sort
- Divide and conquer: mergesort, quicksort
- Introduction to graph theory; basic graph algorithms: traversals and shortest path
🗄️ Database Management and Warehousing
- ER-model, relational model: relational algebra, tuple calculus, SQL
- Integrity constraints, normal forms, file organization, indexing
- Data types, data transformation: normalization, discretization, sampling, compression
- Data warehouse modelling: schema for multidimensional data models, concept hierarchies, computations
🤖 Machine Learning
- Supervised Learning: Regression and classification, simple/multiple linear regression, ridge regression, logistic regression
- k-nearest neighbour, naive Bayes classifier, linear discriminant analysis, support vector machines, decision trees
- Bias-variance trade-off, cross-validation methods (LOO, k-folds)
- Multi-layer perceptron, feed-forward neural networks
- Unsupervised Learning: Clustering algorithms, k-means/k-medoid, hierarchical clustering (top-down, bottom-up)
- Dimensionality reduction, principal component analysis
🧠 Artificial Intelligence
- Search: informed, uninformed, adversarial
- Logic: propositional, predicate
- Reasoning under uncertainty: conditional independence, exact inference (variable elimination), approximate inference (sampling)