SYLLABUS
Master of Computer Applications (MCA) is a two-year professional Master's Degree in computer science awarded in India. The post graduate program is designed to meet the growing demand for qualified professionals in the field of Information Technology.
-- Download Syllabus S1 S2 --
-- Download Syllabus S3 S4 --
-- Download Syllabus - S2 --
VIEWS
SEMESTER I
Questions
- >> Advanced Data Structure (First Semester MCA(2year) Exam July 2020)
- >> Advanced Data Structure (First Semester MCA(2year) Exam December 2021)
- >> Advanced Software Engineering (First Semester MCA(2year) Exam July 2020)
- >> Advanced Software Engineering (First Semester MCA(2year) Exam December 2021)
- >> Digital Fundamentals (First Semester MCA(2year) Exam July 2020)
- >> Digital Fundamentals (First Semester MCA(2year) Exam December 2021)
- >> Mathematics Foundations for Computing (First Semester MCA(2year) Exam July 2020)
- >> 2022 - 23 Mathematics Foundations for Computing, Digital Fundamentals & Computer Architecture, Advanced Data Structure, Advanced Software Engineering (First Semester MCA (Two Year) Degree(R,S) Examination December/January
Mathematical Foundations for Computing
Digital Fundamentals & Computer Architecture
Advanced Data Structures
Advanced Software Engineering
SEMESTER II
Questions
Syllabus Qn & Answers
Advanced Database Management Systems
Advanced Computer Networks
- Module Full
- Module I
- >> PDF 1
- >> PPT 1
- >> PDF 1
- >> DNS PDF 1
- >> DNS PDF 2
- >> FTP PDF
- >> Packet SwitchingTechniques PDF
- >> SMTP and POP PDF
- >> Module 1 part 1
- >> Module 1 part 2
- Module 2
- Module 3
- >> Module 3
- >> DSV
- >> Routing Information Protocol
- >> OSPF
- >> Border Gateway Protocol
- >> LINK STATE ROUTING
- >> Multicast routing
- Module 4
- >> Switches and bridge
- >> data link layer
- >> Data_Encoding
- >> Media, Signal strengthand interference
- >> Router,MAC,ARP,FIB
- Module 5
Elective AOS
Elective AI
SEMESTER III
Syllabus
Questions -Series 1
- >> CYBER SECURITY & CRYPTOGRAPHY.pdf
- >> DATA SCIENCE and MACHINE LEARNING.PDF
- >> Design and Analysis of Algorithms.PDF
- >> DEEP LEARNING.pdf
Questions -Series 1
Questions - S3 2022 Exam
Q&A Syllabus
- >> CRYPTO_1.pdf
- >> CRYPTO_2.pdf
- >> CRYPTO_3.pdf
- >> CRYPTO_4.pdf
- >> CRYPTO_5.pdf
- >> CRYPTO_6.pdf
- >> CRYPTO_7.pdf
- >> CRYPTO_8.pdf
Data Science & Machine Learning
- >> DS_FULL_LECTURE_NOTES (Dr V N Krishnachandran).pdf
- >> Data Science and Machine Learning (Vasudevan T V).pdf
- >> Module 3
- >> Module 4
- >> Module 5
- >> Module 1.1 PPT
- >> Module 1.2 PPT
- >> Module 1 Univariate and Multivariate Data Exploration.pdf
- >> Module 1 data science process.pdf
- >> Module 1 data science.pdf
- >> Module 1 data visualization.pdf
- >> Module 3.1
- >> Module 3.2
- >> Module 3.1 PDF (T)
- >> Module 3.2 PDF (T)
- >> Linear Regression.pdf
- >> Multiple Linear Regression.pdf
Design & Analysis of Algorithms
- >> Module 2
- >> Module 2 Greedy Strategy.pdf
- >> Module 2 Knapsack,Prim's.pdf
- >> Module 2 Kruskals,Job sequencing.pdf
- >> Module 2 Dynamic Programming .pdf
- >> Module 2 All pair shortest path,TSP.pdf
- >> Module 2 Bellman-Ford Algorithm.pdf
- >> Backtracking,N Queens problem.pdf
- >> Branch and Bound, 8-Puzzle.pdf
- >> Sum of Subset_1.pdf
- >> Sum of subset_2.pdf
CYBER SECURITY & CRYPTOGRAPHY
- >> CRYPTO_M1_1 (T)
- >> CRYPTO_M1_2 (T)
- >> CRYPTO_M2_1 (T)
- >> CRYPTO_M2_2 (T)
- >> CRYPTO_M2_3 (T)
- >> CRYPTO_M2_4 (T)
- >> CRYPTO_M2_5 (T)
- >> CRYPTO_M5_1 (T)
- >> Module 1
- >> Module 2
- >> Module 3
- >> Module 4
- >> Module 5
DEEP LEARNING
- >> Module 2 DL-dropout layer.pdf
- >> Module 2 DROPOUT.pdf
- >> Module 2 TensorFlow.pdf
- >> Module 2 Tensorflow Basics.pdf
- >> Module 2 UNDERFITTINGAND EXTRA NOTES.pdf
- >> Module 2 Data visualilzation.pptx
- >> Module 2 VALIDATION CURVE.pptx
- >> Module 2 validation curve_doc.docx
- >> Module 3 CNN.docx
- >> Module 3 CNNs are a class of Deep Neural Networks .docx
- >> Module 3 Casestudy .docx
- >> Module3 Casestudy-AlexNet .docx
- >> Module3 Casestudy-LeNet.docx
- >> Module3 Convolution.docx
SOCIAL NETWORKS
- >> SNA Mod 1 Semantic Web.pdf
- >> SNA Mod 1 Social Network Analysis.pdf
- >> SNA Mod 2 Electronic Sources for Network Analysis.pdf
- >> SNA Mod 3 Modelling and Aggregating.pdf
- >> SNA Mod 4 Graph Structure of Facebook.pdf
- >> SNA Mod 4 Graph Structure of the Web.pdf
- >> SNA Mod 5 Google.pdf
- >> SNA Mod 5 Search Engines.pdf
SEMESTER IV
Main Project
An project is built by students individually by Students on Relevant topics
update soon
.....