GATE Data Science and Artificial Intelligence (DA)

Computer Based Test180 minutes100 marks

• General Aptitude17

  • Verbal Aptitude
    • Basic English grammar (tenses, articles, adjectives, prepositions, conjunctions, verb-noun agreement, parts of speech)
    • Basic vocabulary: words, idioms and phrases in context
    • Reading and comprehension
    • Narrative sequencing
  • Quantitative Aptitude
    • Data interpretation: bar graphs, pie charts, 2-D and 3-D plots, maps, tables
    • Numerical computation and estimation: ratios, percentages, powers, exponents, logarithms, permutations and combinations, series
    • Mensuration and geometry
    • Elementary statistics and probability
  • Analytical Aptitude
    • Logic: deduction and induction
    • Analogy
    • Numerical relations and reasoning
  • Spatial Aptitude
    • Transformation of shapes: translation, rotation, scaling, mirroring, assembling, grouping
    • Paper folding, cutting and patterns in 2 and 3 dimensions
• Probability and Statistics16

  • Probability basics
    • Counting (permutations and combinations)
    • Probability axioms, sample space, events; independent and mutually exclusive events
    • Marginal, conditional and joint probability; Bayes theorem
    • Conditional expectation and variance
  • Descriptive statistics
    • Mean, median, mode, standard deviation
    • Correlation and covariance
  • Random variables and distributions
    • Discrete random variables, PMF; uniform, Bernoulli, binomial
    • Continuous random variables, PDF; uniform, exponential, Poisson, normal, standard normal, t, chi-squared
    • Cumulative distribution function, conditional PDF
  • Statistical inference
    • Central limit theorem
    • Confidence interval
    • z-test, t-test, chi-squared test
• Linear Algebra6

  • Vector space, subspaces, linear dependence and independence
  • Matrices: projection, orthogonal, idempotent, partition matrices and properties
  • Quadratic forms
  • Systems of linear equations; Gaussian elimination
  • Eigenvalues, eigenvectors, determinant, rank, nullity, projections
  • LU decomposition, singular value decomposition
• Calculus and Optimization3

  • Functions of a single variable: limit, continuity, differentiability
  • Taylor series
  • Maxima and minima; single-variable optimization
• Programming, Data Structures and Algorithms5

  • Programming in Python
  • Stacks, queues, linked lists, trees, hash tables
  • Linear search and binary search
  • Selection, bubble, insertion sort; mergesort and quicksort (divide and conquer)
  • Introduction to graph theory; traversals and shortest path
• Database Management and Warehousing5

  • ER model, relational model: relational algebra, tuple calculus, SQL
  • Integrity constraints, normal form
  • File organization, indexing, data types
  • Data transformation: normalization, discretization, sampling, compression
  • Data warehouse modelling: multidimensional schema, concept hierarchies, measures
• Machine Learning10

  • Supervised learning
    • Regression: simple, multiple and ridge regression
    • Logistic regression, k-NN, naive Bayes, LDA, SVM, decision trees
    • Bias-variance trade-off
    • Cross-validation: leave-one-out, k-fold
    • Multi-layer perceptron, feed-forward neural network
  • Unsupervised learning
    • Clustering: k-means / k-medoid
    • Hierarchical clustering: top-down, bottom-up, single and multiple linkage
    • Dimensionality reduction, principal component analysis
• Artificial Intelligence3

  • Search: informed, uninformed, adversarial
  • Logic: propositional, predicate
  • Reasoning under uncertainty: conditional independence, variable elimination, approximate inference by sampling