The GATE (Graduate Aptitude Test in Engineering) examination for Data Science and Artificial Intelligence (DS & AI) is a specialized paper introduced to assess candidates' knowledge and skills in the domains of data science and artificial intelligence. It is designed for those aspiring to pursue higher studies, research, or careers in these fields.
The GATE examination is conducted jointly by the IITs (Indian Institutes of Technology) and IISc (Indian Institute of Science) on a rotational basis.
Educational Qualification: Candidates must have completed or be in the final year of a Bachelor’s degree in Engineering/Technology/Architecture or a Master’s degree in any relevant science subject. Specific eligibility requirements may vary depending on the institute conducting the examination.
The GATE Data Science & AI paper is typically divided into the following sections:
General Aptitude:
Includes questions on verbal and numerical ability.
Core Subject Knowledge:
Includes topics related to Data Science and Artificial Intelligence.
1. General Aptitude:
Verbal Ability: English grammar, sentence completion, verbal reasoning.
Numerical Ability: Arithmetic, algebra, geometry, and data interpretation.
2. Core Subject Knowledge:
Data Science:
Data Exploration: Data cleaning, data transformation, exploratory data analysis.
Statistical Analysis: Descriptive statistics, probability, hypothesis testing.
Machine Learning: Supervised and unsupervised learning, regression, classification, clustering.
Data Visualization: Techniques and tools for visualizing data, such as charts, graphs, and dashboards.
Artificial Intelligence:
Fundamentals of AI: Basic concepts, history, and applications.
Search Algorithms: Problem-solving methods, search techniques (e.g., BFS, DFS).
Knowledge Representation: Ontologies, semantic networks, frames.
Reasoning: Logical reasoning, inference techniques.
Neural Networks: Basics of neural networks, deep learning, and advanced architectures (e.g., CNNs, RNNs).
Mode: Computer-based Test (CBT)
Type of Questions:
Multiple Choice Questions (MCQs)
Numerical Answer Type (NAT) Questions
Duration: 3 hours
Total Marks: 100
Understand the Syllabus: Familiarize yourself with the syllabus for both General Aptitude and Core Subject Knowledge.
Study Plan: Develop a structured study plan covering all topics with time allocated for revision and practice.
Reference Books:
Data Science: "Data Science for Business" by Foster Provost and Tom Fawcett, "Python Data Science Handbook" by Jake VanderPlas.
Artificial Intelligence: "Artificial Intelligence: A Modern Approach" by Stuart Russell and Peter Norvig, "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
Practice Papers: Solve previous years’ question papers and take mock tests to understand the exam pattern and improve time management.
Online Resources: Utilize online courses, tutorials, and practice platforms to enhance your knowledge and skills.
Data Science:
"Data Science for Business" by Foster Provost and Tom Fawcett
"Python Data Science Handbook" by Jake VanderPlas
"Introduction to Data Science" by Laura Igual and Santi Seguí
Artificial Intelligence:
"Artificial Intelligence: A Modern Approach" by Stuart Russell and Peter Norvig
"Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
"Pattern Recognition and Machine Learning" by Christopher M. Bishop
The GATE Data Science & AI examination is a gateway for students and professionals aiming to advance their knowledge and careers in data science and artificial intelligence. With thorough preparation, a clear understanding of the syllabus, and consistent practice, candidates can successfully navigate this competitive examination and pursue opportunities in research, academia, and industry.