Ravindrababu Ravula 

Course available for 1 year after purchase


Instructor Name: Prof Ravindrababu Ravula



Instructor: Syed Peera Saheb





Classes will be held between 6 AM to 8 AM and 6 PM to 10 PM as per educator availability. 
Course Fee: ₹14999 + 18% GST

A comprehensive LIVE program designed for serious GATE DA aspirants who want a structured, consistent, and well-guided preparation experience.

Course Overview

This course is built to cover the entire GATE Data Science & AI syllabus through a carefully structured live learning process.

Each subject is taught with emphasis on:

strong conceptual understanding
clarity in problem-solving
alignment with GATE exam patterns
The goal is not just coverage, but meaningful learning and steady progress throughout the preparation cycle.

Course Format

Fully LIVE classes with topic-wise structured delivery
Interactive doubt-solving sessions
Continuous academic support via discussion forum.
Lecture notes of every class will be provided after every class.
The learning process is designed to be engaging, consistent, and easy to follow.

What You Will Gain

Clear understanding of core Data Science and AI concepts
Ability to approach and solve GATE-level problems confidently
Improved accuracy, speed, and exam temperament
A well-defined preparation flow from start to finish
Course Features

Structured Concept Delivery

Topics are taught in a logical sequence, ensuring continuity and depth of understanding.

Complete Syllabus Coverage

All subjects are covered comprehensively with a focus on relevance to the GATE examination.

Practice After Every Lecture

Each session is followed by assignments designed to reinforce concepts and improve application skills.

GATE PYQs with Detailed Solutions

Previous Year Questions are discussed with complete explanation, helping you understand patterns and expectations.

High-Quality Practice Sets

Curated questions provide additional exposure and strengthen problem-solving ability.

Dedicated Doubt Resolution

Doubts are addressed through live sessions and ongoing discussion support.

Revision Support

Compact and focused revision modules help you revise efficiently closer to the exam.

Syllabus

Verbal Aptitude

Basic English grammar: tenses, articles, adjectives, prepositions, conjunctions, verb-noun
agreement, and other parts of speech
Basic vocabulary: words, idioms, and phrases in context.
Reading and comprehension, Narrative sequencing.

Quantitative Aptitude

Data interpretation: data graphs (bar graphs, pie charts, and other graphs representing data), 2-
and 3-dimensional plots, maps, and tables
Numerical computation and estimation: ratios, percentages, powers, exponents and logarithms,
permutations and combinations, and series Mensuration and geometry Elementary statistics
and probability.

Analytical Aptitude

Logic: deduction and induction, Analogy, Numerical relations and reasoning.
Spatial Aptitude

Transformation of shapes: translation, rotation, scaling, mirroring, assembling, and grouping
paper folding, cutting, and patterns in 2 and 3 dimensions. 


Probability and Statistics: 
Counting (permutation and combinations), probability axioms,
Sample space, events, independent events, mutually exclusive events, marginal,
conditional and joint probability, Bayes Theorem, conditional expectation and variance,
mean, median, mode and standard deviation, correlation, and covariance, random
variables, discrete random variables and probability mass functions, uniform, Bernoulli,
binomial distribution, Continuous random variables and probability distribution function,
uniform, exponential, Poisson, normal, standard normal, t-distribution, chi-squared
distributions, cumulative distribution function, Conditional PDF, Central limit theorem,
confidence interval, z-test, t-test, chi-squared test.

Linear Algebra: 
Vector space, subspaces, linear dependence and independence of vectors,
matrices, projection matrix, orthogonal matrix, idempotent matrix, partition matrix and their
properties, quadratic forms, systems of linear equations and solutions; Gaussian
elimination, eigenvalues and eigenvectors, determinant, rank, nullity, projections, LU
decomposition, singular value decomposition.

Calculus and Optimization: 
Functions of a single variable, limit, continuity and
differentiability, Taylor series, maxima and minima, optimization involving a single variable.

Programming, Data Structures and Algorithms: 
Programming in Python, basic data
structures: stacks, queues, linked lists, trees, hash tables; Search algorithms: linear search
and binary search, basic sorting algorithms: selection sort, bubble sort and insertion sort;
divide and conquer: mergesort, quicksort; introduction to graph theory; basic graph
algorithms: traversals and shortest path.

Database Management and Warehousing: 
ER-model, relational model: relational algebra,
tuple calculus, SQL, integrity constraints, normal form, file organization, indexing, data
types, data transformation such as normalization, discretization, sampl

GATE DA 2027 –  Core Plus (Mentorship Program)

Ravindrababu Ravula

Maharashtra

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