Software Project Management

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Purbanchal University - Master of Computer Application (M.C.A.) Third Semester.

Software Project Management

YEAR: II Semester: I Teaching Schedule

Hours/Week

Examination Scheme

Theory Tutorial Practical Internal Assessment Final Total

3 1 - Theory Practical Theory Practical 100

20 - 80 -

Course: Master of Computer Application (M.C.A.) Credit: 3

Objective:

After accomplishing this course, students will be able to apply software project management techniques.

Contents:

1. Introduction to Software Project Management [4]

Introduction, Projects and software projects, Problems with software projects, Project phases and life cycle, Management and management control, Stakeholders, An overview of project planning.

2. Project Analysis [8]

Introduction, Strategic assessment, Technical assessment, Economic analysis: Present worth, future worth, annual worth, internal rate of return (IRR) method, Benefit-Cost ratio analysis, Including uniform gradient cash flow and comparison of mutually exclusive alternatives.

3. Project Planning and Scheduling [8]

Objectives of activity planning, Work breakdown structure, Bar chart, Network planning model: Critical path method (CPM), Program evaluation and review technique (PERT), Precedence diagramming method (PDM), Shortening project duration, Identifying critical activities.

4. Risk Management [3]

Introduction, Nature and Identification of risk, Risk analysis, Evaluation of risk to the schedule using Z-values.

5. Resource Allocation [3]

Identifying resource requirements, Resource allocation, Resource smoothening and resource balancing.

6. Monitoring and Control [4]

Introduction, Collecting data, Visualizing progress, Cost monitoring, Earned value analysis, Project control.

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Purbanchal University - Master of Computer Application (M.C.A.) Third Semester. Introduction, Types of contract, Negotiating a software contract, Principles of software contract management.

8. Organization Behavior and Personnel Management [5] Understanding behavior, Recruitment, Selection, Training, Motivation and motivation theories, Leadership and leadership styles, Becoming a team, working in groups, Decision making, Organizational structures.

9. Software Quality Management [4]

Introduction, Software reliability, Software quality management system, ISO 9000.

10. Software Configuration Management [3]

Introduction, Need, Basic Configuration, Management function, Baseline, Configuration management responsibilities.

References:

1. Mike Cottrell, Bob Hughes, Software Project Management, Inclination/Thomas Computer Press.

2. Darrel Ince, I. Sharp, M. Woodman, Introduction to software project management and quality assurance, Tata McGraw Hill.

3. Walker Royce, Software Project Management: A unified Framework, Addison-Wesley, An imprint of Pearson Education.

4. Watts S. Humphrey, Managing the Software Process, Addison-Wesley, An imprint of Pearson Education.

5. Willian G. Sullivan, James A. Bontadelli, Wkub M. Wicks, Engineering Economy, Pearson Education Asia.

6. B. C. Punmia, K. K. Khandelwal, Project Planning and control with PERT and CPM, Laxmi Publications (P) Ltd.

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Optimization Technique

YEAR: II Semester: I Teaching Schedule Hours/Week Examination Scheme

20 - 80 -

Objective: After completing this subject, students will be able to apply the concept of linear programming, duality theory, assignment method, queuing theory etc to solve real life business problems.

Contents:

1. The linear programming problem:

Introduction; Formulation of linear programming problem; Benefits and limitations of linear programming; Graphical solutions to linear programming problem; Standard LP Form and its basic solutions; Simplex method; Artificial variable

techniques: Two phase method, big-M method. [9 hours]

2. Duality in linear programming:

Concept of duality; Fundamental properties of duality; duality and simplex

method; Dual simplex method. [6 hours]

3. Transportation problem:

Introduction; Mathematical formulation of transportation model; Transportation problem as a linear programming problem; Finding initial basic feasible solutions: North-West corner, Row-Minima, Column-Minima, Lowest-Cost Entry and Vogel’s approximation methods; Moving towards optimality; Degeneracy.

[9 hours] 4. Assignment problem:

Introduction; Mathematical formulation of assignment model; Solution of assignment problem; Multiple solutions; Hungarian algorithm; Maximization in

assignment model; Impossible assignment. [7 hours]

5. Integer programming:

Introduction; Gomory’s All-I.P.P. method; Construction of Gomory’s constraints; Fractional Cut Method-All integer; Fractional Cut Method-Mixed integer; Branch

and Bound method. [7 hours]

6. Queuing theory:

Introduction; Definition of terms in queuing model; Single infinite channels; Production model: Multichannel service infinite queue, Finite population model.

(Derivations are not required). [8 hours]

Text Book

1. Operation Research by Kanti Swarup, P.K. Gupta, Man Mohan. (Sultan Chand & Sons).

Reference:

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MARKETING MANAGEMENT

Year: II Semester: I Teaching Schedule Hours/Week Examination Scheme

20 - 80 -

Objective: This course aims to develop students’ knowledge and skill in analyzing marketing opportunities and designing appropriate marketing strategies in a dynamic and competitive business environment. The course includes marketing concept, emerging challenges in the 21st Century, marketing mix, marketing environment, marketing information system, market segmentation and targeting, differentiation and positioning, customer analysis, competitor analysis, market analysis, strategy development in the areas of product, price, distribution, and promotion; and marketing planning and control.

Contents:

1. Introduction 3 hrs.

Marketing tasks; meaning of marketing; business orientations and marketing concept; dynamism in business and marketing; marketing mix components and decision areas in marketing; and marketing environment.

2. Marketing Information System 3 hrs.

Components of marketing information system; marketing research process and areas; and new development in information technology; and database marketing.

3. Market Segmentation and Targeting 3 hrs.

Levels and Patterns of market segmentation; segmentation of consumer and business markets; evaluation and selection of market segments.

4. Differentiation and Positioning Strategy 3 hrs

Differentiation tools; positioning variables and process.

5. Customer Analysis 3 hrs.

Customer value, costs and satisfaction; cost of lost customer and customer retention; relationship marketing; and total quality marketing.

6. Competitor analysis 3 hrs.

Identifying, evaluating and dealing with competitors.

7. Market Analysis 3 hrs.

Market size, growth, profitability, cost structures, distribution system and trends; and identification of key success factors.

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8. Product Strategies 6 hrs.

Product concepts and typology; product life cycle strategies; new product development process; consumer adoption and diffusion of innovation processes; Product line and mix strategies; brand building and brand equity; service product management.

9. Pricing Strategies 3 hrs.

Pricing process; pricing policies and strategies; and initiating and responding to price changes in the market.

10. Distribution Strategies 3 hrs.

Distribution components; channel designs and selection; channel role, power, and conflicts.

11. Promotion Strategies 6 hrs.

Promotion objectives; promotion mix: advertising, personal selling, sales promotion, public relations, and direct marketing; selection of promotion strategies.

12. Marketing Planning and Control 6 hrs.

Strategic and tactical marketing plans; planning tools: BCG and GE matrix and portfolio models; the planning process; feedback and control.

Basic Text

Kotler, Philip, Marketing Management (11th edition), Pearson Education. References

Aaker, David A., Strategic Market Management (6th edition), John Wiley & Sons. Blois, Ketith (ed.), The Oxford Textbook of Marketing, Oxford University Press.

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Remote Sensing

Year: II Semester: I Teaching Schedule Hours/Week Examination Scheme

3 1 2 Theory Practical Theory Practical 100

20 20 60 -

Course: Master of Computer Application (M.C.A.) Credit:3

Objective:

After learning the material covered in this module, students should be able to: • explain the fundamental principle of remote sensing

• explain the techniques for applying Remote Sensing data

• to know how to measure the spectral characteristic of typical substances such as soil, grass, water

• perform image analysis with understating of the underlying principles • identify key factors to be considered while using Remote Sensing.

1. Course contents with hour breakdown

Chapter 1 Fundamentals of Remote Sensing A total of 5

lectures 1.1 Introduction, RS & GIS, Spatial data acquisition 3 hrs 1.2 Electro-magnetic Energy, Energy interaction in the

atmosphere

3 hrs 1.3 Energy Interaction with earth surface, Spectral

Reflectance

1.5 hrs

Chapter 2 Sensors and Platforms A Total of 4

Lectures

2.1 Passive and Active Sensors 1.5 hrs

2.2 Platforms, Space-borne Remote sensing, Air-borne Remote Sensing,

1.5 hrs

2.3 Cameras for RS, Film for RS, 1.5 hrs

2.4 Image data characteristic, Data selection criteria 1.5 hrs

Chapter 3 Aerial Photography A Total of 2

Lectures 3.1 Introduction, Vertical and oblique photography, Scale

of photography, True color and color infrared photography

3 hrs

Chapter 4 Multi-spectral Remote sensing A Total of 2

Lectures 4.1 Introduction, Types of multispectral Scanner and

some operational space borne multispectral scanners

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Chapter 5 Microwave Remote Sensing A Total of 2

Lectures 5.1 Radar introduction, microwave radiation, Distortion in

Radar Images

1.5 hrs

5.2 Application of radar images 1.5 hrs

Chapter 6 Data used in remote Sensing A Total of 2

Lectures 6.1 Digital Image data, format of RS image data, Data

recording, storage and distribution media

3 hrs

Chapter 7 Visual Image Interpretation A Total of 2

Lectures 7.1 Information extraction in RS, Image interpretation,

stereoscopic vision, interpretation elements

1.5 hrs

7.2 Application of visual image interpretation 1.5 hrs

Chapter 8 Image processing systems A Total of 2

Lectures

8.1 Image processing in RS, Storage of image data, 3 hrs

Chapter 9 Image Processing- Correction A Total of 2

Lectures 9.1 Radiometric correction, Geometric distortions,

geometric corrections

1.5 hrs

9.2 Georeferencing, geocoding 1.5 hrs

Chapter 10 Image enhancement & visualization A Total of 2

Lectures 10.1 Image enhancement and feature extraction,

visualization of image data, Filter Operation between images

3 hrs

Chapter 11 Digital image classification A Total of 3

Lectures

11.1 Introduction, Image Classification techniques 3 hrs

11.2 Validation of the result 1.5 hrs

Chapter 12 Application of Remote Sensing data A Total of 2

Lectures

12.1 Application of RS data in different aspects 3 hrs

Reference Books

1. Lucas L.F. Janssen, Gerrit C. Huurneman (Ed), 2001: Principles of Remote Sensing: An Introductory textbook, International Institute for Geo-information Science and Earth Observation (ITC), The Netherlands.

2. Japan Association of Remote Sensing (Ed), 1993: Remote Sensing Note: Japan Association of Remote Sensing, Tokyo, Japan.

3. Narayan L.R.A, 1999: Remote Sensing and its Applications: Universities Press (India) Limited, Hydrabad, India.

4. John R. Jensen, 2003: Remote Sensing of the Environment, An Earth Resource Perspective: Pearson Education India.

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5. Patel A.N., Singh, S. 1999: Principles of Remote Sensing: Scientific Publishers India.

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Data Mining and Data warehousing

Year: II Semester: I

Teaching Schedule Hours/Week

Examination Scheme

20 - 80 -

Contents:

1. Introduction (3 Hrs)

• Background

• What is Data Mining?

• Data Mining - On what kind of Data? • Data mining functionalities.

• Are all of the patterns interesting?

2. Data Warehouse and OLAP Technology for Data Mining (3 Hrs) Data warehouse, Multidimensional data model, Data warehouse Architecture and Implementation, Data Cube Technology, From Data Warehouse to Data Mining

3. Data Preprocessing (3 Hrs)

Data Cleaning, Data Integration and Transformation, Data Reduction, Discretization and Concept Hierarchy Generation

4. Data Mining Primitives, Languages and System Architectures (3 Hrs) What defines Data Mining Task? Data Mining Query Language, Architecture of Data Mining Systems.

5. Mining Association Rules in Large Databases (3 Hrs) Association Rule Mining, Mining single-Dimensional Boolean Association Rules from Transactional Databases, Mining Multilevel Association Rules from Transactional Databases, Mining Multilevel Association Rules from Relational Databases and Data Warehouse, From Association Mining to Correlation Analysis. Constraint Based Mining.

6. Classification and Prediction (12 Hrs)

Introduction to Classification and Prediction, Decision Trees, Bayesian Classification, Classification by Backpropagation, Classification based on

Concept from Association Rule Mining, Other Classification methods, Prediction, Classifier Accuracy.

7. Cluster Analysis (3 Hrs)

Introduction – Cluster Analysis, Partitioning Methods, Hierarchical methods, Density-Based Methods, Grid Based Methods, Model Based Clustering methods, outlier Analysis.

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8. Mining Complex Types of Data (9 Hrs)

Multidimensional Analysis and Descriptive Mining of Complex Data objects, Mining Spatial Databases, Mining Multimedia Databases, Mining Time-Series and Sequence Data, Mining Text Database, Mining the World Wide Web.

9. Application and Trends in Data Mining (6 Hrs)

Data Mining Applications, Data Mining System Products and Research

Prototypes, Additional Themes on Data Mining, Social Impacts of Data Mining,

Trends on Data Mining.

Text Book:

“Data Mining: Concepts and Techniques”, Jiawei Han, Micheline Kamber, Elsevier.

Reference:

• “Data Mining “, Pieter Adrianns, Dolf Zantinge, Addison Wesley.

• “Data Warehousing in the Real World”, Sam Anahory, Dennish Murray,

Pearson Education.