skilldevelopment.cpe@lpu.co.in

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

Pushpendra Kumar Pateriya is an academician and administrator currently serving as the Head of the Department (HoD), School of Computer Science and Engineering, Lovely Professional University. With over a decade of experience in higher education, he has been actively involved in teaching, academic leadership, and curriculum development. He has qualified the Graduate Aptitude Test in Engineering (GATE) eight times, achieving a highest All India Rank (AIR) of 568, and is also UGC-NET qualified, reflecting his strong academic foundation and subject expertise. Since 2022, he has been actively mentoring students for the GATE examination and is currently handling his fifth batch of GATE preparatory classes. Under his guidance, students have achieved commendable results, including a highest AIR of 351 in Computer Science, demonstrating his effectiveness in competitive exam training. His areas of expertise include Operating Systems, Data Structures, Algorithms, and High-Performance Computing. He has contributed to faculty development programs and technical training initiatives, particularly in domains such as parallel computing, GPU acceleration, and emerging technologies. Pushpendra Pateriya is a strong advocate of outcome-based and industry-aligned education, emphasizing problem-solving, analytical thinking, and hands-on learning. He focuses on preparing students not only for academic excellence but also for competitive examinations and real-world technological challenges. With extensive experience in academic administration and student mentorship, he continues to work towards building a learning ecosystem that promotes innovation, research orientation, and practical skill development.


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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.