GATE 2025 Data Science and Artificial Intelligence (New Paper) Detailed Syllabus PDF Download
GATE, for short, is the Graduate Aptitude Test in Engineering-the all-important entrance examination for enrollment into postgraduate programs or career opportunities in engineering and science. The year 2025 is the birth year of GATE's new paper on Data Science and Artificial Intelligence reflection of how important these aspects are becoming in today's technological world. These candidates will be tested on essential concepts, methodologies, and applications in data science and AI. As data becomes the backhand of most industries, managing these areas is very crucial. All aspects of the syllabus are covered in detail, and key points are mentioned, along with a way to download the syllabus PDF so that this helps the candidate prepare properly for exams to cope up with this stringent field.
Exam Pattern
the GATE 2025 examination for Data Science and Artificial Intelligence will test both the foundation-related knowledge and practical skills in the given field. Here is a more detailed outline of the question paper format:
1. Types of Questions
- Multiple-choice questions (MCQs)
- Each question has four options, and only one is correct.
Negative Marking:
- 1 mark for each wrongly answered question (in case of 1-mark questions).
- 2 marks for each wrongly answered question (in case of 2-mark questions).
Numerical Answer Type Questions (NATs):
- These questions require a numerical answer and do not provide any choice.
- No negative marking for the wrong answer.
Multiple Select Questions (MSQs):
- There can be a chance that one has to select more than one correct answer from the options.
- Negative marking for the wrong answer.
2. Total Marks
- The total marks for this exam will be 100 marks.
3. Time duration
- 3 hours will be allowed for the candidates to answer the exam.
4. Sections
The paper can be classified into various sections, dealing with a vast range of topics:
- Mathematics for Data Science: This includes Linear Algebra, Calculus, Probability
- Programming and Data Structures: Examples include Python, R, and Algorithms.
- Machine Learning and AI Techniques: Topics include, Supervised Learning, Neural Networks
- Data Management and Big Data Technologies: Some of the topics deal with SQL, Hadoop, Spark
- Data Visualization and Communication: Topics include Visualization Tools, Effective Presentation
- Ethics and Social Implications: this includes Ethical Considerations in AI
5. General Aptitude Section
This section will be present in all the GATE papers. Usually, in this paper, questions of the following types will be covered:
- Verbal Ability
- Numerical Ability
- Reasoning
- This General Aptitude section accounts for 15 of the total marks.
6. Marks Distribution
The approximate outline of marks distribution is as shown below:
- Mathematics: 20-30 marks
- Programming and Data Structures: 15-20 marks
- Machine Learning and AI Techniques: 25-30 marks
- Data Management: 15-20 marks
- Data Visualization: 10-15 marks
- Ethics and Social Implications: 5-10 marks
Also Read : GATE 2025 Mathematics (MA) Detailed Syllabus
Syllabus
1. Mathematics for Data Science
Linear Algebra:
- Vectors, matrices, eigenvalues and eigenvectors
- Matrix operations, determinants, and systems of equations
Calculus:
- Differentiation and Integration
- Partial derivatives and multivariable calculus
Probability and Statistics:
- Probability theory, random variables, distributions (normal, binomial, Poisson)
- Descriptive statistics, inferential statistics, hypothesis testing, regression analysis
2. Programming and Data Structures
Programming Languages:
- Python, R, and relevant libraries (NumPy, Pandas, sci-kit-learn)
Data Structures:
- Arrays, lists, stacks, queues, trees, graphs
- Complexity analysis (Big O notation)
Algorithms:
- Sorting algorithms: quick sort, merge sort
- Algorithms for Search (binary, depth-first, breadth-first search)
3. Machine Learning and AI Techniques
Supervised Learning:
- Regressing techniques, classification algorithms (decision trees, SVM, k-NN)
Unsupervised Learning:
- Clustering techniques (k-means, hierarchical clustering)
Neural Networks:
- neural networks basics, deep learning architectures (CNNs, RNNs).
Natural Language Processing:
- Text processing, Sentiment analysis, word embeddings
4. Data Management and Big Data Technologies
Databases:
- Relational databases, SQL queries, normalization, and denormalization
Big Data Frameworks:
- Hadoop ecosystem (HDFS, MapReduce)
- Spark architecture and operations
Data Warehousing and ETL
- Concepts of Data Warehousing, Data Lakes, and ETL Processes
5. Data Visualization and Communication
Visualization Tools:
- Tableau, Matplotlib, Seaborn
Principles of Data Visualization
- Best practices in the representation of data
Storytelling with Data
- Data narratives that effectively convey meaning from data
6. Ethics and Social Implications
Ethics in AI Models
- Bias, privacy in AI, responsible AI practices
Social Impact
- What is the impact on society and policy-making of data science and AI?
Download the GATE 2025 Syllabus PDF
GATE 2025 Syllabus Data Science and Artificial Intelligence. GATE 2025 Data Science and Artificial Intelligence is downloadable in PDF file format. Download this PDF file from the link given below: GATE 2025 Data Science and Artificial Intelligence. pdf Hopefully, it would give some idea about what to look out for in the exam.
Preparation Tips
- Understand the Syllabus: Familiarise each of the topics concerning their weightage so that the planned study time effective schedule is designed.
- Prepare Study Timetable: Prepare structured timetables addressing all subjects and plan for regular revision.
- Master Solved Previous Year Papers: Solve previous year GATE papers which would give an idea about the pattern and type of questions asked in such exams.
- Concept Clarity: Done only through memorization, and GATE tests this only through an application.
- Online Resource: Video lectures, online courses, and forums give you different descriptions and various techniques of problem-solving.
- Join study groups: Discussions with fellow mates are quite refreshing and encouraging too.
- Mock Tests: Tests taken on simulated examination conditions give control over time management and confidence building.
Best Book
- "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
- A definitive guide covering deep learning architectures and techniques.
- "Machine Learning: A Probabilistic Perspective" by Kevin P. Murphy
- Comprehensive insights into machine learning with a focus on probabilistic models.
- "Python for Data Analysis" by Wes McKinney
- Essential for data manipulation and analysis using Python and its libraries.
- "Pattern Recognition and Machine Learning" by Christopher M. Bishop
- In-depth exploration of pattern recognition techniques and machine learning algorithms.
- "Data Science from Scratch" by Joel Grus
- Hands-on introduction to data science concepts, implemented in Python.
- "Artificial Intelligence: A Modern Approach" by Stuart Russell and Peter Norvig
- A thorough overview of AI principles, techniques, and applications.
Conclusion
GATE 2025 for Data Science and Artificial Intelligence GATE 2025 for Data Science and Artificial Intelligence is a golden opportunity to make an entry into a fast-developing field. Right preparation requires a precise knowledge of the curriculum and some ideal study books. Candidates will get a strong foundation in the theoretical as well as practical approaches using the recommended books.
Call to action:
Begin preparing today by downloading the comprehensive syllabus and then choosing study material. Invest that time to get a grip on core concepts, practice day in and out, and interact with peers in study groups. With dedication to quality resources, unlock doors to success in the GATE exam and move on to a highly successful career stream of Data Science and AI!
FAQ’S
1. What is the GATE 2025 Data Science and AI exam?
- It assesses candidates' knowledge and skills in data science and artificial intelligence for postgraduate admissions.
2. What topics are covered in the syllabus?
- Topics include mathematics, programming, machine learning, data management, visualization, and ethics.
3. How can I download the syllabus PDF?
- Visit the official GATE website and navigate to the syllabus section to download it.
4. What is the exam pattern?
- The exam consists of MCQs, NATs, and MSQs, with a total of 100 marks and a duration of 3 hours.
5. Are there any recommended books?
- Yes, some top choices include "Deep Learning" by Goodfellow and "Machine Learning: A Probabilistic Perspective" by Murphy.
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