KU Integrated Data Science & AI: 2027 Entrance Syllabus

Analogies, syllogisms, statement and conclusions, statement and arguments, blood relations, direction sense, seating arrangements (linear and circular), puzzles, Venn diagrams, coding-decoding, arithmetic number series.

Vocabulary, spot the error, fill in the blanks, synonyms/homonyms, antonyms, spellings, idioms and phrases, one-word substitution, active/passive voice, direct/indirect narration, reading comprehension.

Definition and types of sets, operations on sets, types of relations and functions, domain, range and codomain, composite and inverse functions.

Limits and continuity, differentiation, application of derivatives, integration and differential equations.

Conditional probability, random variables and distributions; data representation through graphs and diagrams (bar charts, pie charts, histograms, frequency polygons, ogives); measures of central tendency (mean, median, mode, geometric and harmonic mean); combined and weighted mean.

Quadratic equations and roots, sequences and series, matrices and determinants: properties and applications.

Types of networks (LAN, MAN, WAN, PAN), topologies and network structures, basics of IP addressing, internet and web communication, browsers, email and messaging tools.

Basic programming concepts and introduction to algorithms, data types, control structures (if, for, while), functions and recursion.

Linear data structures: arrays (1-D and 2-D), matrices, special and sparse matrices; traversal, linear and binary search, bubble sort; stacks (push, pop) and queues (enqueue, dequeue).

Basics of Boolean algebra, common logic gates (AND, OR, NOT, XOR), simple circuit design and truth tables.

Procedural programming versus OOP, benefits of OOP, abstraction, encapsulation, inheritance, polymorphism, C versus C++; C++ elements: tokens, identifiers, variables, constants, data types, streams, operators, decision and loop control structures.

Types of operating systems, functions (process, memory, file and device management), 32-bit and 64-bit systems, firmware, Windows features and versions, Linux, Android, macOS, GUI and kernel.

Overview of Python: features, history and applications in web development, data science and automation; basic syntax, variables and data types, operators.

Number systems (binary, octal, decimal, hexadecimal) and conversions, binary arithmetic and complements, data representation, range, accuracy, underflow and overflow.

Big data, artificial intelligence, machine learning, natural language processing, AR/VR, robotics, Internet of Things and cloud computing with its service types.