GATE Data Science & Artificial Intelligence (DA) Syllabus 2024 with Marks Weightage, Download PDF

GATE Data Science & Artificial Intelligence (DA) Syllabus 2024: This year, IISc Bangalore has added a new subject for GATE 2024 exam which is the Data Science & Artificial Intelligence (DA). Since it is a new subject, therefore, the interested candidates should go through the detailed GATE Data Science and AI syllabus that is given here. Candidates appearing for GATE 2024 exam who opt for DA as their first choice can choose CS, EC, EE, MA, ME, ST and XE as their second choice in GATE two-paper combination. Get to know about the important topics, paper pattern, etc for GATE Data Science and AI subject in this article.

GATE Data Science & Artificial Intelligence (DA) Syllabus 2024

The GATE syllabus for Data Science & Artificial Intelligence (DA) 2024 covers the topics like  Probability and Statistics, Linear Algebra, Calculus and Optimization, Programming, Data Structures and Algorithms, Database Management and Warehousing, Machine Learning, and Artificial Intelligence. It is essential for all the candidates, who are going to appear in the GATE Data Science & Artificial Intelligence exam, that they must be well-versed with GATE Data Science & Artificial Intelligence syllabus before starting their preparation. Check the important topics for GATE Data Science & Artificial Intelligence syllabus.

GATE Data Science & Artificial Intelligence (DA) Syllabus 2024 Section Wise 

GATE Data Science & Artificial Intelligence (DA) exam contains two parts i.e. General Aptitude and core Data Science & Artificial Intelligence subjects. The weightage of General Aptitude and core Data Science & Artificial Intelligence is 15% and 85% respectively. The detailed list of topics of GATE Data Science & Artificial Intelligence syllabus is provided below.

Data Science & Artificial Intelligence

• Probability and Statistics: Counting (permutation and combinations), probability axioms, Sample space, events, independent events, mutually exclusive events, 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 distribution, Continuous random variables and probability distribution function, uniform, exponential, Poisson, normal, standard normal, t-distribution, chi-squared distributions, cumulative distribution function, Conditional PDF, Central limit theorem, confidence interval, 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 and their properties, 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 form, file organization, indexing, data types, data transformation such as normalization, discretization, sampling, compression; data warehouse modelling: schema for multidimensional data models, concept hierarchies, measures: categorization and computations.
• Machine Learning: (i) Supervised Learning: regression and classification problems, simple linear regression, multiple linear regression, ridge regression, logistic regression, k-nearest neighbour, naive Bayes classifier, linear discriminant analysis, support vector machine, decision trees, bias-variance trade-off, cross-validation methods such as leave-one-out (LOO) cross-validation, k-folds crossvalidation, multi-layer perceptron, feed-forward neural network; (ii) Unsupervised Learning: clustering algorithms, k-means/k-medoid, hierarchical clustering, top-down, boƩom-up: single-linkage, multiplelinkage, dimensionality reduction, principal component analysis.
• Artificial Intelligence: Search: informed, uninformed, adversarial; logic, propositional, predicate; reasoning under uncertainty topics - conditional independence representation, exact inference through variable elimination, and approximate inference through sampling.

GATE Data Science & Artificial Intelligence (DA) Syllabus 2024: Official PDF

This year IISc Bangalore has introduced a new subject Data Science & Artificial Intelligence (DA) in GATE 2024 exam. The official GATE Data Science & Artificial Intelligence syllabus PDF has been released on the official website. Here, we provide you the direct link to download GATE Data Science & Artificial Intelligence 2024 syllabus.

How to Prepare the GATE Data Science & Artificial Intelligence (DA) Syllabus 2024?

As Data Science & Artificial Intelligence (DA) is a newly added subject in GATE 2024 exam, so all the interested aspirants need to follow a well-planned approach to excel in the GATE Data Science & Artificial Intelligence (DA) exam. Here, we are giving you some tips for GATE preparation for Data Science & Artificial Intelligence (DA) paper.

• Understand the Syllabus: First of all the aspirants must thoroughly review the complete GATE Data Science & Artificial Intelligence syllabus. Note out the important GATE Data Science & Artificial Intelligence topics, giving priority to those needing more attention. Make a study plan around these priorities.
• Create a Study Schedule: Once you go through the entire syllabus, create a complete study plan that covers all the topics given in the GATE Data Science & Artificial Intelligence syllabus. Allocate ample time to each subject/topic as per your convenience.
• Focus on Fundamental Understanding: Always focus on understanding the core principles of each topic. Only memorizing things will not be enough for this exam. 
• Create Revision Notes: Develop a habit of making short revision notes with important formulas, concepts, and important points for quick last-minute review.
• Take Mock Tests: The candidates must take enough mock tests to get familiar with the real exam environment. After each mock test, you should analyze your performance and work on improving them. This practice will also help to improve time management abilities.

Best Books to Prepare the GATE Data Science & Artificial Intelligence (DA) Syllabus 2024

The selection of right study material plays a vital role in preparing for the GATE Data Science & Artificial Intelligence exam. Below is a list of some highly recommended books for GATE Data Science & Artificial Intelligence syllabus.

GATE Data Science & Artificial Intelligence (DA) Exam Pattern 

The GATE Data Science & Artificial Intelligence paper includes questions based on General Aptitude and Data Science & Artificial Intelligence. The GATE Data Science & Artificial Intelligence paper comprises 65 questions with a total score of 100 marks. Candidates have 3 hours to complete the online exam. The question types include Multiple choice questions, Multiple select questions, and Numerical Answer Type questions. Refer to the table below for more information on the GATE Data Science & Artificial Intelligence exam pattern.

Why has Data Science and Artificial Intelligence been included as subjects in the GATE 2024 entrance exam?

According to a report published by India Brand Equity Foundation (IBEF), there will be 31.4% rise in AI-focused data science and mathematical jobs by 2030. In 2021, the global AI market was valued at approximately $59.67 billion, and it is projected to maintain a rapid growth rate of 39.4%. By the year 2028, this market could achieve an impressive size of around $422.37 billion. India's AI market is also experiencing a surge, having been valued at $3.1 billion in 2020, and it's estimated to grow at about 20.2% every year, reaching US$ 7.8 billion by 2025. The addition of Data Science and Artificial Intelligence as subjects in the GATE 2024 entrance exam likely reflects the growing importance of these fields in various industries.

Also check: The candidates can also check the detailed syllabus of the following subjects.