MCA 2ND SEM PREVIOUS YEAR KE PAPER KI PDF NICHE DI GYI HAI PLEASE CHEAK
MCA 2 SEM SUBJECTS AND PREVIOUS YEAR PAPER
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BIG DATA ANALYTICS SYLLABUS AND PREVIOUS PAPER CLICK
- INTRODUCTION TO BIG DATA
Introduction to BigData Platform – Challenges of
Conventional Systems - Nature of DataEvolution Of Analytic Scalability -
Intelligent data analysis- Analytic Processes and Tools -Analysis vs Reporting
- Modern Data Analytic Tools - Statistical Concepts: Sampling Distributions - Re-Sampling
- Statistical Inference - Prediction Error.
- MINING DATA STREAMS
Introduction To Streams
Concepts – Stream Data Model and Architecture - Stream Computing - Sampling
Data in a Stream – Filtering Streams – Counting Distinct Elements in a Stream –
Estimating Moments – Counting Oneness in a Window – Decaying Window - Real time
Analytics Platform(RTAP) Applications - Case Studies - Real Time Sentiment
Analysis, Stock Market Predictions.
- INTRODUCTION TO BIG DATA ANALYTICS & R
PROGRAMMING
Analyzing, Visualization and
Exploring the Data, Statistics for Model Building and Evaluation, Introduction
to R and RStudio, Basic analysis in R, Intermediate R, Intermediate analysis in
R, Advanced Analytics - K-means clustering, Association rules-Speedup, Linear Regression,
Logistic Regression, Naïve Bayes, Decision Trees, Time Series Analysis, Text
Analysis
- HADOOP History of Hadoop- The Hadoop Distributed
File System – Components of Hadoop Analyzing the Data with Hadoop- Scaling
Out- Hadoop Streaming- Design of HDFS-Java interfaces to HDFSBasics-
Developing a Map Reduce Application-How Map Reduce Works-Anatomy of a Map
Reduce Job run-Failures-Job Scheduling-Shuffle and Sort – Task execution -
Map Reduce Types and Formats- Map Reduce Features.
- FRAMEWORKS
Applications on Big Data Using Pig and Hive – Data
processing operators in Pig – Hive services – HiveQL – Querying Data in Hive -
fundamentals of HBase and ZooKeeper - IBM InfoSphereBigInsights and Streams.
Visualizations - Visual data analysis techniques, interaction techniques;
Systems and applications
CLOUD COMPUTING
Unit-
I
CLOUD
COMPUTING FUNDAMENTALS
Cloud
Computing definition, private, public and hybrid cloud. Cloud types; IaaS,
PaaS, SaaS. Benefits and challenges of cloud computing, public vs private
clouds, role of virtualization in
enabling the cloud; Business Agility: Benefits and challenges to Cloud
architecture. Application availability, performance, security and disaster
recovery; next generation Cloud Applications.
Unit-II
CLOUD
APPLICATIONS
Technologies
and the processes required when deploying web services; Deploying a web service
from inside and outside a cloud architecture, advantages and disadvantages.
Unit-
III
MANAGEMENT OF CLOUD SERVICES
Reliability,
availability and security of services deployed from the cloud Performance and
scalability of services, tools and technologies used to manage cloud services
deployment; Cloud Economics : Cloud Computing infrastructures available for
implementing cloud based services. Economics of choosing a Cloud platform for
an organization, based on application requirements, economic constraints and
business needs (e.g Amazon, Microsoft and Google, Salesforce.com, Ubuntu and
Redhat)
Unit
IV
APPLICATION DEVELOPMENT
Service
creation environments to develop cloud based applications. Development
environments for service development; Amazon, Azure, Google App.
Unit
V
CLOUD
IT MODEL
SOFTWARE ENG METHODLOGY SYLLABUS
UNIT-1 Introduction
The Evolving Role of Software, Software Characteristics,
Changing Nature of Software, Software Engineering as a Layered Technology,
Software Process Framework, Framework and Umbrella Activities, Process Models,
Capability Maturity Model Integration (CMMI).
UNIT-2
Requirement Analysis Software Requirement Analysis,
Initiating Requirement Engineering Process, Requirement Analysis and Modeling
Techniques, Flow Oriented Modeling, Need for SRS, Characteristics and
components of SRS.
UNIT-3
Risk
Management Software
Risk, Risk Identification, Risk Projection and Risk Refinement RMMM Plan.
Quality Management: -
Quality Concepts,
Software Quality Assurance, Software Reviews, Metrics for Process and Projects.
UNIT-4
Design
Engineering
Design Concepts, Architectural Design Elements, Software
Architecture, Data Design at the Architectural Level and Component Level,
Mapping of Data Flow into Software Architecture, Modeling Component Level
Design.
UNIT-5 Testing Strategies
& Tactics
DATA STRUCTURE AND ALGORYTHM
Unit I:
Introduction to Data structures
Definition, Classification of data structures:
primitive and non-primitive, Elementary data organization, Time and space
complexity of an algorithm (Examples), String processing. Dynamic memory allocation and pointers:
Definition of dynamic memory allocation, Accessing the address of a variable,
Declaring and initializing pointers, Accessing a variable through its pointer,
Meaning of static and dynamic memory allocation, Memory allocation functions:
malloc(), calloc(), free() and realloc(). Recursion: Definition, Recursion in C
(advantages), Writing Recursive programs – Binomial coefficient, Fibonacci,
GCD.
Unit II:
Searching and Sorting
Basic Search Techniques: Sequential search: Iterative
and Recursive methods, Binary search: Iterative and Recursive methods,
Comparison between sequential and binary search. Sort: General background and
definition, Bubble sort, Selection sort, Insertion sort, Merge sort, Quick
sort.
Unit III: Stack and Queue
Stack
– Definition, Array representation of stack, Operations on stack: Infix, prefix
and postfix notations, Conversion of an arithmetic expression from Infix to
postfix, Applications of stacks. Queue: Definition, Array representation of
queue, Types of queue: Simple queue, Circular queue, Double ended queue
(deque), Priority queue, Operations on all types of Queues. and
display. Unit IV: Linked List
Definition,
Components of linked list, Representation of linked list, Advantages and
Disadvantages of linked list. Types of linked list: Singly linked list, doubly
linked list, Circular linked list, Operations on singly linked list: creation, insertion, deletion, search
Unit V: Tree
Graphs and their Applications:
GRAPHIC AND VISUAL COMPUTING SYLLABUS
Unit I
Fundamentals of Computer Graphics: Applications of computer Graphics
in various, Video Display Devices, Random scan displays, raster scan displays,
DVST, Flat Panel displays, I/O Devices.
Graphics Primitives: Algorithms for drawing Line, circle, ellipse,
arcs & sectors, Boundary Fill & Flood Fill algorithm, Color Tables
Unit II
Transformations & Projections: 2D & 3D Scaling, Translation,
rotation, shearing & reflection, Composite transformation, Window to View
port transformation, Orthographic and Perspective Projections.
Clipping: CohenSutherland, Liang Barsky, Nicholl-Lee-Nicholl
Line clipping algorithms, Sutherland Hodgeman, Weiler Atherton Polygon clipping
algorithm.
Unit III
Three Dimensional Object Representations: 3D Modeling transformations, Parallel & Perspective projection, Clipping in 3D. Curved lines & Surfaces, Spline representations, Spline specifications, Bezier Curves & surfaces, B-spline curves & surfaces, Rational splines, Displaying Spline curves & surfaces.
Unit IV
Basic Rendering: Rendering in nature, Polygonal representation, Affine and coordinate system transformations, Visibility and occlusion, depth buffering, Painter’s algorithm, ray tracing, forward and backward rendering equations, Phong Shading per pixel per vertex Shading.
UNIT V
