LPU - Lovely Professional University
What you'll learn

The GATE Preparatory Course is designed to provide comprehensive coverage of the Computer Science and Engineering syllabus, aligned with the Graduate Aptitude Test in Engineering (GATE). The course focuses on building strong conceptual foundations in core subjects such as Data Structures, Algorithms, Operating Systems, Computer Networks, Database Management Systems, Theory of Computation, Compiler Design, and Engineering Mathematics. The program emphasizes a problem-solving approach through intensive practice of previous years’ questions (PYQs), mock tests, and concept-driven discussions. It also integrates structured learning methodologies, including peer discussions, analytical thinking, and time-bound practice sessions, to enhance exam readiness and performance. Course Learning Outcomes (CLOs) After successful completion of this course, students will be able to: Understand and apply core concepts of Computer Science and Engineering required for GATE examination. Solve complex problems efficiently using appropriate data structures, algorithms, and mathematical techniques. Analyze and interpret problems from subjects like Operating Systems, DBMS, Computer Networks, and Theory of Computation. Demonstrate proficiency in Engineering Mathematics, including discrete mathematics, linear algebra, probability, and calculus. Apply time management and exam strategies to solve questions accurately under time constraints. Evaluate and improve performance through regular mock tests and analysis of previous years’ GATE questions. Develop analytical and logical reasoning skills essential for competitive examinations.

  • Propositional and first order logic. Sets, relations, functions, partial orders and lattices. Monoids, Groups. Graphs: connectivity, matching, colouring. Combinatorics: counting, recurrence relations, generating functions.
  • Matrices, determinants, system of linear equations, eigenvalues and eigenvectors, LU decomposition.
  • Limits, continuity and differentiability, Maxima and minima, Mean value theorem, Integration.
  • Random variables, Uniform, normal, exponential, Poisson and binomial distributions. Mean, median, mode and standard deviation. Conditional probability and Bayes theorem.
  • Boolean algebra. Combinational and sequential circuits. Minimization. Number representations and computer arithmetic (fixed and floating point).
  • Machine instructions and addressing modes. ALU, data-path and control unit. Instruction pipelining, pipeline hazards. Memory hierarchy: cache, main memory and secondary storage; I/O interface (interrupt and DMA mode).
  • Programming in C. Recursion. Arrays, stacks, queues, linked lists, trees, binary search trees, binary heaps, graphs.
  • Searching, sorting, hashing. Asymptotic worst case time and space complexity. Algorithm design techniques: greedy, dynamic programming and divide-and-conquer. Graph traversals, minimum spanning trees, shortest paths.
  • Regular expressions and finite automata. Context-free grammars and push-down automata. Regular and context-free languages, pumping lemma. Turing machines and undecidability.
  • Lexical analysis, parsing, syntax-directed translation. Runtime environments. Intermediate code generation. Local optimization, Data flow analyses: constant propagation, liveness analysis, common sub expression elimination.
  • System calls, processes, threads, inter-process communication, concurrency and synchronization. Deadlock. CPU and I/O scheduling. Memory management and virtual memory. File systems.
  • ER-model. Relational model: relational algebra, tuple calculus, SQL. Integrity constraints, normal forms. File organization, indexing (e.g., B and B+ trees). Transactions and concurrency control.
  • Concept of layering: OSI and TCP/IP Protocol Stacks; Basics of packet, circuit and virtual circuit- switching; Data link layer: framing, error detection, Medium Access Control, Ethernet bridging; Routing protocols: shortest path, flooding, distance vector and link state routing; Fragmentation and IP addressing, IPv4, CIDR notation, Basics of IP support protocols (ARP, DHCP, ICMP), Network Address Translation (NAT); Transport layer: flow control and congestion control, UDP, TCP, sockets; Application layer protocols: DNS, SMTP, HTTP, FTP, Email

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Pushpendra Kumar Pateriya
Assistant Professor

Mr. Pushpendra Kumar Pateriya is an accomplished academic leader, Linux systems educator, and mentor with rich expertise in Operating Systems, Linux Administration, System Programming, Virtualization, and Cloud Computing. He has an outstanding academic record, having qualified GATE 9 times with an impressive highest All India Rank (AIR) of 568, along with being UGC NET qualified, reflecting his strong command over core computer science fundamentals. As the Head of the Department, he has successfully designed and delivered multiple industry-oriented training programs in Linux, RHCSA, OS Labs, and System Administration, with a strong emphasis on hands-on practical learning and certification readiness. His core expertise includes Red Hat Enterprise Linux (RHEL), shell scripting, process and service management, networking, SELinux, storage administration, boot troubleshooting, and enterprise server security. His pedagogy focuses on real-world administration scenarios, lab-driven troubleshooting, and project-based learning, enabling students to prepare for RHCSA, DevOps, cloud, and cybersecurity roles. He has consistently mentored students for Linux-based placements, open-source technologies, competitive exams, and industry certifications, making this training highly aligned with enterprise expectations.


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Dr. Prince
Professor

Dr. Prince Singh is an academician currently serving as an Associate Professor at Lovely Professional University, with experience in teaching, research, and academic mentoring. He is actively involved in delivering quality education in the domain of Computer Science and Engineering, contributing to curriculum development and student-centric learning initiatives. His teaching approach emphasizes conceptual clarity, problem-solving, and practical application of knowledge, enabling students to build a strong academic foundation. Dr. Singh is committed to fostering an engaging learning environment through interactive pedagogy, peer learning, and outcome-based education practices. He continuously works towards enhancing students’ analytical thinking and technical skills, preparing them for both competitive examinations and industry requirements. With a focus on academic excellence and continuous improvement, he contributes to the development of a robust educational ecosystem that supports innovation, skill development, and research-oriented learning.