The GATE examination in the year 2024 also featured one new topic known as Data Science and Artificial Intelligence (AI). The topic was included because many students and teachers nowadays use Artificial intelligence and Data Science in studies or other spheres of life. Since GATE Data Science and AI was introduced last year, we have analyzed the question paper for you to have a clear idea of the topic. The main focus and adjustments are given to structure, major topics, challenging levels, and preparations.

Check: Download the GATE Data Science and AI Syllabus PDF

The idea is to illustrate the intensity of particular topics as well as provide valuable insights into how the student queries should be tackled. The overview of GATE Data Analysis and Artificial Intelligence is as follows:

GATE Data Science and AI Paper Structure

The GATE 2024 DA paper consisted of approximately 65 questions. These questions evaluate candidates' knowledge across various aspects of Data Science and AI. The structure included:

1. Multiple Choice Questions (MCQs): Assessing theoretical concepts and definitions.
2. Numerical Answer Type Questions (NATs): Requiring computational skills and problem-solving abilities.
3. General Aptitude: Evaluating logical reasoning and quantitative skills.

Check GATE Data Science and AI Question Paper:

Fundamentals of Data Science

• Statistics: Such topics cannot be overemphasized they include mean, median, variance, and standard deviation.
• Data Preprocessing: Activities involved in making data ready for analysis.
• Exploratory Data Analysis (EDA): Data pattern and analysis for the purpose of visual representation.

Machine Learning

• Supervised Learning: Regression techniques or classification techniques like for instance are among the most notable set of algorithm.
• Unsupervised Learning: K- means and hierarchical clustering algorithms.
• Evaluation Metrics: All that is needed to know about precision, recall, F1-score, and ROC curves.

Deep Learning

• Neural Networks: Elementary types such as Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN).
• Frameworks: Knowledge about tools such as TensorFlow and PyTorch.
• Optimization Techniques: Learning rate Schedule, Back propagation and Regularization.

Data Mining

• Clustering and Classification: Methods of classification and categorization.
• Association Rule Mining: Understanding market basket analysis.
• Anomaly Detection: Outliers in Datasets.

NLP (Natural Language Processing)

• Text Representation: Such methods as TF-IDF and word vectors.
• Language Models: With sequence-to-sequence models and with transformers.
• Applications: Sentiment analysis, chatbot and machine translation.

Ethics and Socio-Cultural Issues in Artificial Intelligence

• Algorithmic Bias: Challenges that relate to the question of fairness and the question of transparency.
• Privacy Concerns: Among the concepts that are clearly distinguished yet interconnected there is data protection and compliance in the context of finding the ways for its effective implementation.
• Regulatory Frameworks: Awareness of common ethical use of AI and how to apply them.

Check Some Data Science and AI Books:

GATE Data Science and AI Weightage

The weightage of various topics and the number of questions will differ in the coming years. GATE Preparation tips can help you accelerate your preparation process. The number of questions and weightage in GATE Data Science and AI question 2024 are:

Topic Wise Questions and Marks

You must be aware of the questions and their types that were included in GATE DA and AI last year. You can check Resources and Study Materials for GATE preparation.

GATE 2024 Data Science and AI Topic-Wise Distribution

Most students join GATE online Coaching class for preparation. First, you should understand what questions you might get in the exam. This table provides a clear overview of both the weightage and the estimated number of questions for each topic, assisting candidates in planning their preparation effectively.

Difficulty Level

The GATE 2024 DA paper was characterized as having a moderate to high difficulty level. Candidates faced a well-distributed range of questions that tested both theoretical knowledge and practical application. The numerical answer-type questions, in particular, were challenging, requiring candidates to demonstrate advanced problem-solving skills.

Check: GATE 2025 Exam Dates