Master of Computer Applications

MC – 301 : Software Engineering

Objective

To know the software engineering methodologies for the development of quality, cost effective and schedule meeting software.

Course Structure

Unit	Topic	Description
Unit I	Software Engineering and its models	Evolution of Software Engineering, Software development models, Capability maturity models, Software process technology.
Unit II	Software Requirements, Design and Testing	Engineering the product, Modeling the system architecture, Software prototyping and specification, Different types of project metrics, Software project estimation, Models for estimation, Automated tools for estimation. Data design, Architectural design, Interface design, HCI design, Modular design, Testing techniques, Testing for specialized environments, Debugging.
Unit III	Software Project Planning	Different types of project metrics, Software project estimation, Models for estimation, Automated tools for estimation.
Unit IV	Risk management and Project Scheduling	Identification of Software risks, monitoring of risks, Management of risks, formulating a task set for the project, Choosing the tasks of software engineering, Scheduling methods, The Software project plan, Software Quality Assurance, Formal technical reviews, Software reliability, Software quality standards.
Unit V	Software Change Management and Advanced Software Engineering	Baselines, Version control, change control, Auditing and reporting, Web Software Engineering, Mobile Software Engineering, CASE Tools, Clean room Software engineering, Component based Software engineering, Re-engineering, Reverse engineering.

MC – 302 : Machine Learning

Objective

To Study the machine learning Concepts to discover patterns in our data and then make predictions based on often complex patterns to answer business questions, detect and analyze trends and help solve problems.

Course Structure

Unit	Topic	Description
Unit I	Python for Machine Learning	Introduction to python, the concept of data types; variables, assignments; immutable variables; numerical types; arithmetic operators and expressions; understanding error messages; Conditions, Boolean logic, logical operators; ranges; Control statements: if-else, loops (for, while); short circuit evaluation; Strings and text files; manipulating files and directories, os and sys modules; text files: reading/writing text and Numbers from/to a file; creating and reading a formatted file (csv or tab-separated). String manipulations: subscript operator, indexing, slicing a string; strings and number System: converting strings to numbers and vice versa. Binary, octal, hexadecimal numbers Lists, tuples, and dictionaries; basic list operators, replacing, inserting, removing an element; searching and sorting lists; dictionary literals, adding and removing keys, accessing and Replacing values, Oops, Python Numpy and Pandas, Data Preprocessing, Data Manipulation, Data Visualization.
Unit II	Bayes Decision Theory & Linear Machines	Overview and Introduction to Bayes Decision Theory: Machine intelligence and applications, pattern recognition concepts classification, regression, feature selection, supervised learning class conditional probability distributions, Examples of classifiers bayes optimal classifier and error, learning classification approaches. Linear machines: General and linear discriminants, decision regions, single layer neural network, linear separability, general gradient descent, perceptron learning algorithm, mean square criterion and widrow-Hoff learning algorithm; multi-Layer perceptions: two-layers universal approximators, backpropagation learning, on-line, off-line error surface, important parameters.
Unit III	Decision Trees & Instance-based Learning	Learning decision trees: Inference model, general domains, symbolic decision trees, consistency, learning trees from training examples entropy, mutual information, ID3 algorithm criterion, C4.5 algorithm continuous test nodes, confidence, pruning, learning with incomplete data. Instance-based Learning: Nearest neighbor classification, k-nearest neighbor, nearest neighbor error probability.
Unit IV	Machine Learning Concepts and Limitations	Learning theory, formal model of the learnable, sample complexity, learning in zero-bayes and realizable case, VC-dimension, fundamental algorithm independent concepts, hypothesis class, target class, inductive bias, occam's razor, empirical risk, limitations of inference machines, approximation and estimation errors, Tradeoff.
Unit V	Machine Learning Assessment and Improvement	Statistical model selection, structural risk minimization, bootstrapping, bagging, boosting.

MC – 303 : Visual Programming

Objective

It aims to teach basic of visual programming concepts and to design object oriented visual programs with Visual Studio programming development environment and coding.

Course Structure

Unit	Topic	Description
Unit I	GUI Concepts	GUI concept - Data types - GUI Architecture - Message Processing - Keyboard and Mouse Handling Displaying Text and Graphics - File and Printer Handling - DDE – DDL ODBC COM/DCOM / CORBA.
Unit II	.NET Framework	.NET Namespaces, Assemblies, .NET Memory Management, Process Management, Interoperation with COM. Transactions in .NET, Structures Exception Handling, Code Access Security, Web Controls using the .NET framework, The .NET Framework Class Library.
Unit III	VB.NET Programming	VB.NET - Variables and Operators, functions, Decision and Loop statements, Inheritance, Value Types, Operator Overloading, Exception Handling, Arrays and Collections, Properties, Delegates and Events, Windows Forms, Dialog Boxes and Controls, Graphical Output, Files, DATA ACCESS.
Unit IV	C#.NET Programming	C#.NET - Variables, Operators and Expressions, Writing Methods and Applying Scope, Decision statements, Iteration statements, Managing errors and Exceptions values and references, Value types with enumerations and Structures, Arrays and Collections parameter arrays, Inheritance, Garbage collection and Resource management.
Unit V	ASP.NET Applications	Introducing ASP.NET - Understanding validation controls - Accessing Data with web forms - Building ASP.NET applications - Building and XML web service handling XML.

MC – 304 : Web Technologies

Objective

To study the World Wide Web as a platform for interactive applications, content publishing and social services. The development of web-based applications requires knowledge about the underlying technology and the formats and standards the web is based upon.

Course Structure

Unit	Topic	Description
Unit I	HTML5 and CSS3	Introduction: Web Publishing, Web Browsers, Web Servers, URL; Essential Web Developer Tools; Web hosting. HTML5 and CSS3: Introduction, Basics – Structure, Essential Tags, Lists, Links; Formatting Text with HTML and CSS, Including Style Sheets in a Page, Varieties of Selectors, Units of Measure, Box Model, Using Images on Web Pages, Image Formats, Using Images – Basics, Text Alignments, Links, Scale, Backgrounds, Bullets; Image-map, Image Etiquettes. Tables, Creating Table, Parts of Table; Formatting Tables – Size, Borders, Cells; Alignment and Spacing; Spanning; Advanced Enhancements.
Unit II	CSS Layouts and HTML5 Forms	Using CSS to Position Elements: Positioning Schemes, Absolute Positioning, Fixed Positioning, Controlling Stacking, Creating Drop-Down Menus. Designing HTML5 Forms: Basics; Creating Controls, Buttons and Fields; Grouping Controls; Displaying Updates; Applying Styles. Structuring a Page with HTML5: History, Laying Out a Page, Structural Tags, Page Outline, Structural Elements. Advanced CSS Page Layouts: Laying Out Page, The Role of CSS in Web Design.
Unit III	JavaScript and jQuery	JavaScript – Significance, Basics, Environment, Events, Validating Forms, Hiding and Showing Content, Adding New Content to a Page. Using jQuery: Introduction, JavaScript Libraries, Selecting Elements from the Document, Binding Events, Modifying Styles on the Page, Modifying Content on the Page, Special Effects, AJAX and jQuery.
Unit IV	PHP Programming	PHP: Introduction, Basics, Loops, Built-In Functions, User-Defined Functions, Processing Forms, Using PHP Includes, Database Connectivity, Regular Expressions, Sending Mail, Object-Oriented PHP, Cookies and Sessions, File Uploads.

MC – 305 : Program Elective – 2

MC – 306 : Program Elective – 3

MC – 307 : Programming Lab – 1

Visual Programming

Python

MC – 308 : Programming Lab – 2

Web Technology