GATE Data Science & AI 2026 Study Planner

Welcome to your personalized study planner for GATE Data Science and Artificial Intelligence (DA) 2026. This comprehensive tool will help you organize your preparation, track your progress, and maximize your study efficiency.

Your journey starts in April 2025 and continues until the exam in February 2026. With the right strategy and consistent effort, you'll be well-prepared to excel in the exam and secure admission to top universities.

About GATE DA

The Graduate Aptitude Test in Engineering (GATE) for Data Science and Artificial Intelligence (DA) is a prestigious national-level examination in India that tests the comprehensive understanding of various undergraduate subjects in engineering and science for admission to postgraduate programs in Indian institutes.

The GATE DA paper assesses candidates on:

  • Probability and Statistics
  • Linear Algebra
  • Calculus and Optimization
  • Programming, Data Structures and Algorithms
  • Database Management and Warehousing
  • Machine Learning
  • Artificial Intelligence
  • General Aptitude

Exam Pattern:

  • Total Marks: 100
  • Duration: 3 hours
  • Question Types: Multiple Choice Questions (MCQs) and Numerical Answer Type (NAT)
  • General Aptitude: 15% weightage
  • Subject Questions: 85% weightage
  • Negative Marking: For MCQs only (1/3 for 1-mark questions, 2/3 for 2-mark questions)

Your Preparation Journey

This planner is specifically designed for a B.Tech student graduating in June 2025, with a job requiring 8.5 hours daily and a morning gym routine. Your preparation will be structured in four key phases:

1

Foundation Phase (April - June 2025)

Build a strong foundation in the mathematical subjects that underpin Data Science and AI.

2

Programming & Database Phase (July - August 2025)

Leverage your existing Python and SQL knowledge while filling any gaps in these domains.

3

Advanced Topics (September - December 2025)

Focus on the core Data Science and AI topics that constitute the majority of the exam.

4

Revision & Mock Tests (January 2026)

Consolidate your knowledge and build exam temperament through intensive practice.

This planner includes:

  • A daily schedule tailored to your constraints
  • A subject-wise roadmap with hour allocations
  • Curated free resources for each subject
  • Progress tracking tools
  • Note-taking functionality
  • An interactive calendar

Your dedication today will shape your opportunities tomorrow!

Your Personalized Study Plan

Daily Schedule

Weekday Schedule
Time Activity
5:00 AM - 6:00 AM Wake up, freshen up
6:00 AM - 8:00 AM Gym
8:00 AM - 9:00 AM Breakfast, get ready
9:00 AM - 9:30 AM Commute to work
9:30 AM - 6:00 PM Work (8.5 hours including breaks)
6:00 PM - 8:30 PM Commute back (arrive home by 8:30 PM)
8:30 PM - 9:00 PM Dinner, relax
9:00 PM - 11:00 PM Study (2 hours)
11:00 PM - 5:00 AM Sleep
Weekend Schedule
Time Activity
8:00 AM - 9:00 AM Wake up, breakfast
9:00 AM - 1:00 PM Study (4 hours)
1:00 PM - 2:00 PM Lunch break
2:00 PM - 6:00 PM Study (4 hours)
6:00 PM - 8:00 PM Gym/leisure
8:00 PM - 10:00 PM Study/revision (optional 2 hours)
10:00 PM - 11:00 PM Relax, sleep

Study Hours Summary

Weekly Hours
  • Weekdays: 5 days × 2 hours = 10 hours
  • Weekends: 2 days × 8 hours = 16 hours
  • Total per week: 26 hours
Monthly Hours
  • Average weeks/month: 4.3
  • Total per month: ~112 hours
Total Preparation
  • April - December: ~936 hours
  • January: ~112 hours
  • Total: ~1048 hours

Subject-wise Roadmap

Subject Hours Allocation Percentage Notes
Probability and Statistics 100 hours 10.6% Foundation subject, high weightage
Linear Algebra 100 hours 10.6% Essential for ML/AI understanding
Calculus and Optimization 100 hours 10.6% Critical for gradient-based algorithms
Programming, DS & Algorithms 50 hours 5.3% Reduced due to existing Python knowledge
Database Management 50 hours 5.3% Reduced due to existing SQL knowledge
Machine Learning 175 hours 18.6% Emphasized due to high weightage
Artificial Intelligence 175 hours 18.6% Core focus area
General Aptitude 140 hours 14.9% 15% of GATE paper
Buffer/Flexibility 58 hours 5.5% For unexpected events/difficult topics
Total 948 hours 100% April 2025 - January 2026

Study Phases

Phase 1: Foundations (April - June 2025)

Build a strong foundation in the mathematical subjects that underpin Data Science and AI.

Subject Hours Monthly Breakdown Key Topics
Probability and Statistics 100 hours ~33 hours/month Probability axioms, Random variables, Distributions, Statistical testing
Linear Algebra 100 hours ~33 hours/month Vectors, Matrices, Eigenvalues, SVD, Linear transformations
Calculus and Optimization 100 hours ~33 hours/month Limits, Derivatives, Integrals, Optimization techniques
General Aptitude 36 hours ~12 hours/month Verbal ability, Numerical ability, Reasoning
Phase 1 Strategy:
  • Focus on building strong conceptual understanding
  • Use visualization tools to grasp complex mathematical concepts
  • Practice basic problem-solving daily
  • Create concise notes with formulas and key concepts
  • Weekly self-assessment through practice problems
Phase 2: Programming & Databases (July - August 2025)

Leverage your existing Python and SQL knowledge while filling any gaps in these domains.

Subject Hours Monthly Breakdown Key Topics
Programming, DS & Algorithms 50 hours ~25 hours/month Python specifics, Data structures, Search/Sort algorithms, Graph algorithms
Database Management 50 hours ~25 hours/month ER modeling, Relational algebra, SQL, Data warehousing, Normalization
General Aptitude 24 hours ~12 hours/month Continue with regular practice
Buffer/Revision of Phase 1 ~50 hours ~25 hours/month Revisit difficult math concepts, Practice more problems
Phase 2 Strategy:
  • Use your existing Python knowledge to focus on GATE-specific programming questions
  • Implement key data structures and algorithms in Python
  • Practice database design and optimization problems
  • Create sample SQL queries for common database operations
  • Begin integrating Phase 1 knowledge with programming applications
Phase 3: Advanced Topics (September - December 2025)

Focus on the core Data Science and AI topics that constitute the majority of the exam.

Subject Hours Monthly Breakdown Key Topics
Machine Learning 175 hours ~44 hours/month

September: Regression models, Classification basics

October: SVM, Decision trees, Ensemble methods

November: Neural networks, Deep learning

December: Unsupervised learning, Dimensionality reduction
Artificial Intelligence 175 hours ~44 hours/month

September: Search algorithms (informed, uninformed)

October: Logic, propositional & predicate

November: Reasoning under uncertainty

December: Advanced AI topics
General Aptitude 48 hours ~12 hours/month Continue regular practice with previous GATE questions
Phase 3 Strategy:
  • Connect mathematical foundations to ML/AI applications
  • Implement key algorithms to reinforce understanding
  • Practice derivations of important formulas
  • Focus on understanding model behavior, advantages, and limitations
  • Begin solving previous years' GATE questions on these topics
  • Create flowcharts for complex algorithms
Phase 4: Revision & Mock Tests (January 2026)

Consolidate your knowledge and build exam temperament through intensive practice.

Activity Hours Focus
Comprehensive Revision 40 hours Review of all subjects using concise notes and formula sheets
Previous Year Questions 30 hours Systematic solving of previous GATE questions (CS/IT papers for similar topics)
Mock Tests 30 hours Full-length mock tests under timed conditions
Weak Areas Focus 12 hours Targeted practice for identified weak areas
Phase 4 Strategy:
  • Take at least 8-10 full-length mock tests
  • Analyze each mock test to identify weak areas
  • Focus on timing and accuracy
  • Revise formulas and key concepts daily
  • Practice question selection strategy (which questions to attempt first)
  • Focus on maintaining peak mental and physical health

Study Tips & Strategies

General Tips
  • Use commute time for listening to educational podcasts or audiobooks
  • Take short 5-minute breaks every 25 minutes of study (Pomodoro technique)
  • Create concise notes and review them regularly
  • Join online forums or study groups for collaborative learning
  • Use visualization techniques for complex mathematical concepts
  • Solve at least 5 practice problems daily
  • Regularly review your progress and adjust your plan if needed
  • Maintain a healthy sleep schedule and diet
  • Use spaced repetition for better retention of concepts
  • Set specific, achievable goals for each study session
Subject-Specific Strategies
  • For ML/AI: Implement algorithms in Python to reinforce understanding
  • For Mathematics: Practice derivations and create formula sheets
  • For Algorithms: Visualize execution using flowcharts or animation tools
  • For Databases: Practice SQL queries on actual database systems
  • For Aptitude: Daily practice of at least 5 problems across different topics
  • For Statistics: Use real-world examples to understand concepts
  • For Linear Algebra: Utilize visualizations for eigenvalues and transformations
  • For Calculus: Focus on interpreting derivatives and integrals geometrically
Leveraging Your Strengths
  • Use your Python experience to quickly master programming topics
  • Apply SQL knowledge to solve complex database problems efficiently
  • Connect AI/ML theory to practical implementations you're familiar with
  • Share your knowledge by teaching concepts to others (reinforces learning)
  • Use your existing coding skills to implement algorithms from scratch
  • Create practical projects that apply theoretical concepts
  • Utilize your technical background to understand mathematical foundations quickly
Avoiding Common Pitfalls
  • Don't skip fundamentals even if topics seem familiar
  • Avoid marathon study sessions; consistency is more effective
  • Don't neglect General Aptitude (15% of total marks)
  • Beware of information overload; focus on understanding core concepts
  • Don't ignore health and sleep; they directly impact learning efficiency
  • Avoid last-minute cramming before the exam
  • Don't waste time on very obscure topics with low probability of appearing
  • Refrain from comparing your progress with others constantly
Weekly Routine Recommendation
  • Start each week by setting specific, measurable goals for that week
  • Reserve Sunday evenings for weekly review and planning the next week
  • Dedicate at least one weekday to reviewing previously studied material
  • Practice at least 20 GATE-style questions every weekend
  • Take one practice test or solve one previous year paper every two weeks
  • Allocate time for regular fitness and relaxation
  • Connect with study partners or mentors once a week for doubt clearing
  • Rotate subjects throughout the week to maintain interest and prevent burnout

Free Study Resources

Below is a curated collection of free, high-quality resources accessible in India to help you prepare for each subject in the GATE DA 2026 syllabus.

Probability and Statistics

Linear Algebra

Calculus and Optimization

Programming, Data Structures, and Algorithms

Database Management and Warehousing

Machine Learning

Artificial Intelligence

General Aptitude

Mock Tests & Practice Papers

Practice Strategies:
  • Use CSE/IT papers for practicing overlapping topics like ML, programming, and databases
  • Take timed tests to build exam temperament and time management skills
  • Analyze mistakes thoroughly after each mock test
  • Create a bank of frequently-appearing questions and concepts
  • Practice both MCQs and numerical answer type questions

Online Forums & Communities

Study Group Formation Tips:
  • Form a small study group (3-5 people) with similar goals and schedules
  • Meet virtually once a week to discuss difficult topics
  • Assign different subjects to members to create and share summaries
  • Practice explaining concepts to each other (teaching reinforces learning)
  • Conduct mock interviews and problem-solving sessions

Study Progress Tracker

Overall Progress

0 topics completed 0%

Weekly Goals
Hours Logged

Total Hours Logged: 0

Target: 936 hours

Topic Tracker

Study Notes

Your Notes

Note-Taking Tips

  • Use the Cornell note-taking system: divide your notes into main points, details, and summary
  • Create concise formula sheets for each mathematical subject
  • Include examples to illustrate complex concepts
  • Review and rewrite your notes periodically to reinforce learning
  • Use diagrams, flowcharts, and mind maps for visual learning
  • Highlight key definitions, formulas, and facts for quick revision
  • Add page references to textbooks or online resources for deeper exploration
  • Maintain a glossary of important terms and concepts

Study Calendar

Phase Overview

Phase 1

Apr - Jun 2025

Foundations

Phase 2

Jul - Aug 2025

Programming & Databases

Phase 3

Sep - Dec 2025

Advanced Topics

Phase 4

Jan 2026

Revision & Mock Tests

April 2025

Sun
Mon
Tue
Wed
Thu
Fri
Sat

Weekly Study Suggestions

Weeks 1-4 (April)
  • Introduction to Probability: Basic concepts, axioms, and properties
  • Counting techniques: Permutations and combinations
  • Vector spaces and subspaces basics
  • Limits and continuity in calculus
  • General Aptitude: Verbal ability practice
Weeks 5-8 (May)
  • Random variables and probability distributions
  • Matrices, determinants, and eigenvalues
  • Derivatives and applications
  • Single variable optimization
  • General Aptitude: Numerical ability practice
Weeks 9-12 (June)
  • Statistical hypothesis testing and confidence intervals
  • Linear transformations and SVD
  • Advanced optimization techniques
  • General Aptitude: Analytical and logical reasoning
  • Review of Phase 1 topics and practice problems
Weeks 13-16 (July)
  • Python programming: Data types, functions, and advanced features
  • Basic data structures: Stacks, queues, linked lists
  • Search algorithms: Linear and binary search
  • Review of challenging topics from Phase 1
  • General Aptitude: Regular practice
Weeks 17-20 (August)
  • Basic sorting algorithms and divide-and-conquer techniques
  • ER modeling and relational model
  • SQL queries and database design
  • Data warehousing concepts
  • Prepare for transition to ML and AI topics
Weeks 21-24 (September)
  • Supervised Learning: Regression models
  • Classification basics: Logistic regression, k-NN
  • Search algorithms in AI: Uninformed search
  • Continue regular General Aptitude practice
Weeks 25-28 (October)
  • Support Vector Machines and Decision Trees
  • Ensemble methods: Random forests, boosting
  • Logic: Propositional and predicate logic
  • Practice implementing ML algorithms
Weeks 29-32 (November)
  • Neural networks fundamentals
  • Deep learning basics
  • Reasoning under uncertainty
  • Start solving previous GATE questions
Weeks 33-36 (December)
  • Unsupervised learning: Clustering algorithms
  • Dimensionality reduction techniques
  • Advanced AI topics and applications
  • Begin systematic revision of all subjects
Weeks 37-38 (January - First Half)
  • Comprehensive revision of all subjects
  • Practice with formula sheets and quick-recall techniques
  • First round of full-length mock tests
  • Identify and focus on weak areas
Weeks 39-40 (January - Second Half)
  • Intensive practice of previous year questions
  • Multiple mock tests under timed conditions
  • Final revision of high-weightage topics
  • Preparation strategy for exam day

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