Content Specialist | Updated On - Oct 24, 2024
The introduction of Data science and AI in GATE has been game-changing for aspiring candidates. If you are someone who is preparing for GATE Data Science and AI, this article will help you with online and offline resources, books, and study materials. There are many overlaps when it comes to data science and artificial intelligence (AI). AI has many smaller subsets, like machine learning and deep learning. Data science uses these technologies to interpret and analyze data and find trends and patterns to make predictions.
The demand for data science and AI skills is only increasing by the day. If you are preparing for GATE 2025 or looking for a preparation strategy, this is the best place to get a roadmap and tips for your preparation.
Check GATE Data Science and AI Previous Question Paper
| Year | Original Paper | Answer key |
|---|---|---|
| 2024 | DA Question Paper 2024 | DA Answer Key 2024 |
- Understanding the GATE Data Science and AI Syllabus
1.1 Core Subjects for GATE Data Science Preparation
- GATE Data Science and AI Study Materials
2.1 Essential Textbooks for GATE Preparation
2.2 Online Courses for GATE Data Science And AI Preparation
2.3 GATE Data Science and AI Practice Tests
- GATE Data Science Effective Study Strategies
Understanding the GATE Data Science and AI Syllabus
Before proceeding with GATE Preparation, you must be aware of the GATE Data Science and AI Syllabus in and out. All the topics, sub-topics, question patterns, and types must be understood before starting the preparation. You can analyze GATE 2024 Data Science and Artificial Intelligence (DA) Paper to understand the pattern.
| GATE 2025 Artificial Intelligence and Data Science(DA) Exam Pattern | |
|---|---|
| Total Marks | 100 |
| Total Questions | 65 Questions:
|
| Types of Question |
|
| Marks Distribution |
|
| Negative Marking | Applicable only to wrongly answered MCQ
|
Core Subjects for GATE Data Science Preparation
- Mathematics for Data Science: Linear algebra, calculus, probability, statistics, and discrete mathematics.
- Programming and Data Structures: Proficiency in Python, data structures, and algorithms.
- Machine Learning Techniques: Supervised learning, unsupervised learning, and neural networks.
- Data Engineering Fundamentals: Data wrangling, database management, big data technologies.
- Artificial Intelligence Concepts: Search algorithms, knowledge representation, and reasoning.
Check: GATE Previous Question Papers
| Exam Year | GATE Previous Year Question Papers with Solutions |
|---|---|
| GATE 2024 | Download Question Paper |
| GATE 2023 | Download Question Paper |
| GATE 2022 | Download Question Paper |
| GATE 2021 | Download Question Paper |
| GATE 2020 | Download Question Paper |
GATE Data Science and AI Study Materials
The GATE Data Science and AI syllabus covers multiple topics like Mathematics, Deep Learning, Python etc. Some of the essential GATE books and topics for preparation are provided below.
Essential Textbooks for GATE Preparation
You must have a strong concept of Data Science and AI. Sources like Books and practice papers will help you update with the basic concepts. Here are the top textbooks for GATE Data Science:
- Mathematics for Machine Learning by Marc Peter Deisenroth - This covers essential mathematical concepts needed for data science.
- Pattern Recognition and Machine Learning by Christopher Bishop - This is a key to understanding statistical approaches in machine learning.
- Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville - It is an essential book for mastering deep learning techniques.
- Data Science from Scratch by Joel Grus - This book offers hands-on programming examples to grasp data science concepts effectively.
- GATE 2024 Data Science and AI by D. Gupta and R. Sharma - It is a comprehensive guide featuring previous years’ questions and practice problems.
- GATE Data Science and AI Topic-wise Solved Papers by A. Mehta - This book Includes detailed explanations to help solidify your understanding
Some other latest GATE AI books for GATE preparation, including their authors, where they are available, and approximate prices:
| Book Title | Author(s) | Available At | Price (Approx.) |
|---|---|---|---|
| GATE Data Science and AI | D. Gupta, R. Sharma | Amazon, Flipkart | ₹600 - ₹800 |
| Machine Learning for GATE | A. Mehta | Amazon, Flipkart | ₹500 - ₹700 |
| Data Science and AI: A Comprehensive Guide | P. Kumar | Amazon, Pearson | ₹800 - ₹1,200 |
| Artificial Intelligence: A Modern Approach | Stuart Russell, Peter Norvig | Amazon, Flipkart | ₹1,200 - ₹1,500 |
| Deep Learning | Ian Goodfellow et al. | Amazon, Wiley | ₹1,500 - ₹2,000 |
| Statistics and Machine Learning for GATE | V. Jain | Amazon, Flipkart | ₹500 - ₹900 |
| GATE AI: Previous Year Solved Papers | S. Singh | Amazon, GATE Academy | ₹400 - ₹600 |
Online Courses for GATE Data Science And AI Preparation
There are multiple GATE online coaching platforms where you can register for a course. These platforms will provide training on GATE Data Science and AI along with study materials, mock tests, previous questions, and important topics. Some of the questions that you might get in GATE Data Science and AI are:
Check: GATE Question Papers
| GATE Data Science and AI Topics | GATE Data Science and AI Questions |
|---|---|
| Mathematics |  |
| Programming and Data Structures |  |
| Machine Learning |
Question:In supervised learning, which of the following methods is used to evaluate the performance of a regression model? Answer Choices: A) Cross-validation B) Confusion matrix C) ROC curve D) Silhouette score |
| Data Engineering |
Question:Which of the following is NOT a characteristic of a NoSQL database? Answer Choices: A) Schema-less B) High scalability C) ACID compliance D) High availability |
| Artificial Intelligence |
Question:Which search algorithm guarantees to find the least-cost path to the goal if one exists? Answer Choices: A) Depth-first search B) Breadth-first search C) A* search D) Uniform-cost search |
| Probability and Statistics |
Question:If a dataset follows a normal distribution with a mean of 50 and a standard deviation of 5, what percentage of the data falls between 45 and 55? Answer Choices: A) 68% B) 95% C) 99.7% D) 50% |
| Data Analysis |
Question:Which of the following techniques is primarily used for dimensionality reduction? Answer Choices: A) K-Means clustering B) Principal Component Analysis (PCA) C) Decision Trees D) Linear Regression |
GATE Data Science and AI Practice Tests
Effective preparation includes regular practice. Here are some suggestions for mock tests for GATE Data Science:
- GATE Previous Years’ Papers: Solving past papers gives insight into the exam pattern and types of questions asked.
- Online Mock Test Platforms: Websites like Testbook, Gradeup, and Unacademy offer tailored mock tests for GATE Data Science and AI.
- GATE Prep Apps: Consider using apps like “GATE Mentor” and “GATE Academy” for interactive quizzes and practice questions.
GATE Data Science Effective Study Strategies
You must follow a few strategies for GATE preparation. Creating a study plan, understanding the syllabus, and knowing the types of questions are some of the basics of GATE preparation.
- Understand the syllabus and exam pattern
- Create a Study Plan for GATE Preparation
- Follow regular revision to understand weak areas
- Follow group study to know the subject better
- Hands-on practice for programming at platforms like LeetCode, HackerRank, or Kaggle to implement coding exercises.
Check: GATE 2024 Data Science and Artificial Intelligence Question Paper PDF- Download Here
We have provided a structured study plan for GATE Data Science and AI for both three-month and six-month preparation timelines.
Three-Month Study Plan
| Week | Topics | Activities | Goals |
|---|---|---|---|
| 1 | Mathematics: Linear Algebra, Calculus | Study key concepts, solve basic problems | Understand foundational math |
| 2 | Probability and Statistics | Study distributions, statistical measures | Master key statistical concepts |
| 3 | Programming Fundamentals | Learn Python basics, functions, data structures | Build programming confidence |
| 4 | Data Structures and Algorithms | Study arrays, linked lists, sorting algorithms | Implement basic algorithms |
| 5 | Machine Learning Basics | Learn supervised vs. unsupervised learning | Understand ML concepts |
| 6 | Data Engineering | Study ETL processes, SQL basics | Learn data manipulation |
| 7 | Advanced Machine Learning | Focus on neural networks, clustering | Gain practical ML experience |
| 8 | Artificial Intelligence Concepts | Study search algorithms, knowledge representation | Familiarize with AI techniques |
| 9 | Revision: Mathematics & Statistics | Review key formulas, solve mixed problems | Reinforce knowledge |
| 10 | Revision: Programming & Data Structures | Review data structures and coding challenges | Solidify programming skills |
| 11 | Mock Tests and Analysis | Take 2-3 full-length mock tests, analyze performance | Identify weak areas |
| 12 | Final Revision | Focus on weak topics, key algorithms, and formulas | Prepare for the exam |
Six-Month Study Plan
| Month | Week | Topics | Activities | Goals |
|---|---|---|---|---|
| 1 | 1-2 | Mathematics: Linear Algebra, Calculus | Study concepts, solve exercises | Build a strong math foundation |
| 3 | Probability and Statistics | Study probability distributions, statistical measures | Master key statistics | |
| 4 | Python Programming Basics | Learn syntax, data types, and basic functions | Gain programming proficiency | |
| 2 | 5 | Data Structures | Study advanced data structures (trees, graphs) | Understand data management |
| 6 | Algorithms | Focus on sorting and searching algorithms | Implement key algorithms | |
| 7 | Algorithm Complexity | Analyze complexities, big O notation | Develop algorithmic thinking | |
| 8 | Hands-on Projects | Implement small projects using learned concepts | Apply knowledge practically | |
| 3 | 9 | Machine Learning | Study supervised learning (regression, classification) | Understand foundational ML |
| 10 | Unsupervised Learning | Learn clustering techniques (K-Means, Hierarchical) | Explore unsupervised methods | |
| 11 | Data Engineering | Focus on ETL processes, introduction to NoSQL | Learn data pipeline concepts | |
| 12 | SQL Databases | Study SQL queries, data manipulation | Master database interactions | |
| 4 | 13 | Neural Networks | Study basics of neural networks, and frameworks (TensorFlow, PyTorch) | Understand deep learning |
| 14 | Advanced ML Techniques | Explore ensemble methods, SVM | Gain deeper ML insights | |
| 15 | AI Concepts | Study search algorithms, heuristic methods | Familiarize with AI strategies | |
| 16 | Problem-Solving | Work on previous year's GATE questions | Enhance problem-solving skills | |
| 5 | 17 | Revision: Mathematics & Statistics | Review key formulas and concepts | Solidify understanding |
| 18 | Revision: Programming & Data Structures | Review data structures and algorithms | Strengthen coding skills | |
| 19 | Revision: Machine Learning | Recap ML techniques, key algorithms | Reinforce ML knowledge | |
| 20 | Revision: AI Concepts | Review AI algorithms and methodologies | Prepare for complex concepts | |
| 6 | 21 | Mock Tests | Take full-length mock tests, time yourself | Simulate real exam conditions |
| 22 | Test Analysis | Analyze test results, identify weak areas | Focus on improvement areas | |
| 23 | Key Concept Review | Revise important topics and formulas | Finalize knowledge retention | |
| 24 | Last-minute Revision | A quick recap of all subjects, relax before the exam | Prepare mentally for the exam |
Preparing for GATE should start by understanding the topics and question pattern. Once you have an understanding and a clear concept, you can prepare a study plan and follow that.
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