How to prepare for GATE Data Science and Artificial Intelligence (DA)?

If one wants to have an excelling career as a data scientist or in artificial intelligence, exceeding GATE is very important. The entire student community, especially the candidates who are interested in making their career as AI, data scientist or data analyst were waiting for this event. Later they got a good news that Science and Artificial Intelligence (DA) will be launched by IISc Bangalore in the year 2024. This year also this GATE exam will be conducted. Looking at the future prospects and high demand for Data Science and AI, IISC Bangalore has made it clear to add this subject for budding data scientists and AI experts.

MADE EASY blog will help you strategize your preparation for data science and artificial intelligence. Although the introduction of DA has brought a wave of positivity and hope amongst the students.

Syllabus of Data Science and Artificial Intelligence

GATE on their official website has uploaded the syllabus pdf briefly mentioning the topics, which somewhere eases the students to filter out the topics to prepare for.

GATE New Test Paper on (DA) Data Science and Artificial Intelligence Syllabus
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	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 cross-validation, multi-layer perceptron, feed-forward neural network; Unsupervised Learning: clustering algorithms, k-means/k-medoid, hierarchical clustering, top-down, bottom-up: single-linkage, multiple linkage, dimensionality reduction, principal component analysis.
AI	Search: informed, uninformed, adversarial; logic, propositional, predicate; reasoning under uncertainty topics – conditional independence representation, exact inference through variable elimination, and approximate inference through sampling.

 

Preparation Plan

1. Comprehending the DA syllabus: Before preparing for any competitive exam, it is very important to look for the syllabus and determine which concepts need to be covered. While the GATE Data Science and AI syllabus has already been discussed, it is also important to identify the significant topics and lay a solid basis for the DA exam to expect the best outcome.
2. Drafting the study schedule: After thoroughly understanding the syllabus and important topics, we proceed to create the study plan. An organized and strategic study plan helps the aspirants in establishing their goals and achieving them. Effectively allocating the given time to the topics according to their importance and proficiency level benefits both the desired result and in the long run.
3. Choosing the appropriate study resource: Selecting the right study method and preparation strategy is essential. Studying via online classes or books, once decided, helps strike the balance. Using the selected resource effectively helps the mind maintain concentration instead of wandering off and seeking out additional resources.
4. Grasping the fundamentals: Strengthening the basics and essential concepts in each topic is very important, ensuring that the fundamentals are strong and you are confident enough to perform your best for the desired outcome.
5. Practical Knowledge: Reading and revising the concepts, making notes, and memorizing all these are essential. However, acquiring practical information by reading and memorization is not an ideal approach. It’s also necessary to practice coding and apply concepts using well-known libraries like TensorFlow, scikit-learn, and Python.
6. Revising and Mock Tests: Frequent revision of the concepts enhances the vast knowledge that you have studied. Memorizing helps the mind retain knowledge. A mock test provides you with an ambiance and environment similar to the real examination, which really helps. All the mistakes you want to make, all the panic that you might have, and all the inhibitions about the exam are drained at the mock test. Mock tests result in a no-panic zone when you give the final exam. This will be useful in giving your best to the exam and scoring good marks in the GATE DA exam.
7. Staying updated with the current trends: Data Science and Artificial Intelligence are fields that have been constantly growing and evolving. Therefore, staying current with emerging trends, techniques, and research is essential for anyone aspiring to become a data scientist or an expert in AI. Following the recent developments through journals, research papers, and online platforms, keeping an eye on emerging trends, and participating in workshops held by experts in the data science and artificial intelligence fields will benefit the GATE exam 2025.
8. Healthy Lifestyle and Stress Management: It is very usual to experience worry and anxiety during the preparation process. And, while studying, the majority of students neglect their health.  Maintaining a healthy diet and controlling your stress levels will not only help you in the long run, but they will also give you the peace of mind you need to give your best effort and achieve the desired results.

Last year IISC Bangalore added DA as a subject for GATE 2024 which resulted in a very favorable result for the candidates. This year also, Data Science and Artificial Intelligence will be included in GATE 2025 considering its gradual development and bright future. If you are eligible for GATE exam then you must choose Data Science and Artificial Intelligence for GATE 2025.

Keeping in mind the future aspect of DA, MADE EASY has also launched the Online Recorded Course for GATE 2025 Data Science & Artificial Intelligence, which has been curated thoroughly by our expert R&D team. The online recorded course is designed to fulfill the exam perspective to ensure the best outcome. The course features a systematic subject sequence by renowned faculties and the ability to watch the recorded lectures twice.

MADE EASY provides several online courses related to the GATE 2025 Exam. To explore the comprehensive list of courses available, click here.

 

FAQs

1. What are the benefits of GATE Data Science and AI?

As we all know, technology has been rapidly changing and growing, so the demand for data scientists and AI experts is increasing. DA has eventually been included in our day-to-day lives, making the machines do smart things like understand the language and make decisions. It is more likely that a computer has been performing the human task completely. So this has been benefiting businesses like healthcare, education, logistics, entertainment, media, etc.

 

2. Who is eligible for GATE Data Science and AI?

Aspirants must have a Bachelor’s degree in Science (Post-Diploma/4 years after 10+2) or B.Sc. (Research) / B.S. in a relevant field. This includes degrees in Science and Engineering. Candidates studying currently in 3rd year or higher of their qualifying Bachelor’s degree program in the specified fields. Bachelors in degree in science, basically post- diploma is also eligible.

Hope you found this blog post useful.

Please feel free to read over other blogs as it will help in your GATE 2025 exam preparation.

All the best for the competitive exam!

 

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