Software Engineering Syllabus

Software Engineering

UNIT-I: Software and Software Engineering: The Nature of Software, The Unique Nature of WebApps, Software Engineering, Software Process, Software Engineering Practice, Software Myths. Process Models: A Generic Process Model, Process Assessment and Improvement, Prescriptive Process Models, Specialized Process Models, The Unified Process, Personal and Team Process Models, Process Terminology, Product and Process.

UNIT-II: Requirements Analysis And Specification: Requirements Gathering and Analysis, Software Requirement Specification (SRS), Formal System Specification. Software Design: Overview of the Design Process, How to Characterise of a Design?, Cohesion and Coupling, Layered Arrangement of Modules, Approaches to Software Design

UNIT – III: Function-Oriented Software Design: Overview of SA/SD Methodology, Structured Analysis, Developing the DFD Model of a System, Structured Design, Detailed Design, Design Review, over view of Object Oriented design. User Interface Design: Characteristics of Good User Interface, Basic Concepts, Types of User Interfaces, Fundamentals of Component-based GUI Development, A User Interface Design Methodology.

UNIT – IV: Coding And Testing: Coding, Code Review, Software Documentation, Testing, Unit Testing, Black-Box Testing, White-Box Testing, Debugging, Program Analysis Tool, Integration Testing, Testing Object-Oriented Programs, System Testing, Some General Issues Associated with Testing

UNIT – V: Software Reliability And Quality Management: Software Reliability, Statistical Testing, Software Quality, Software Quality Management System, ISO 9000, SEI Capability Maturity Model. Computer Aided Software Engineering: Case and its Scope, Case Environment, Case Support in Software Life Cycle, Other Characteristics of Case Tools, Towards Second Generation CASE Tool, Architecture of a Case Environment.

UNIT – VI Software Maintenance: Software maintenance, Maintenance Process Models, Maintenance Cost, Software Configuration Management. Software Reuse: what can be reused? Why almost No Reuse So Far? Basic Issues in Reuse Approach, Reuse at Organization Level.

TEXT BOOKS:

1. Software engineering A practitioner’s Approach, Roger S. Pressman, Seventh Edition McGrawHill International Edition.

2. Fundamentals of Software Engineering, Rajib Mall, Third Edition, PHI.

3. Software Engineering, Ian Sommerville, Ninth edition, Pearson education

REFERENCE BOOKS:

1. Software Engineering : A Primer, Waman S Jawadekar, Tata McGraw-Hill, 2008

2. Software Engineering, A Precise Approach, Pankaj Jalote, Wiley India, 2010.

3. Software Engineering, Principles and Practices, Deepak Jain, Oxford University Press.

4. Software Engineering1: Abstraction and modeling, Diner Bjorner, Springer International edition, 2006

Data Wrangling Syllabus

DATA WRANGLING

UNIT-1

Introduction to Data Wrangling: What Is Data Wrangling?- Importance of Data Wrangling -How is Data Wrangling performed?- Tasks of Data Wrangling-Data Wrangling Tools-Introduction to Python-Python Basics-Data Meant to Be Read by Machines-CSV Data-JSON Data-XML Data.

UNIT-2

Working with Excel Files and PDFs: Installing Python Packages-Parsing Excel Files-Parsing Excel Files -Getting Started with Parsing-PDFs and Problem Solving in Python-Programmatic Approaches to PDF Parsing-Converting PDF to Text-Parsing PDFs Using pdf miner-Acquiring and Storing Data-Databases, A Brief Introduction-Relational Databases, MySQL and PostgreSQL-Non-Relational Databases, NoSQL-When to Use a Simple File-Alternative Data Storage.

UNIT-3

Data Cleanup: Why Clean Data? - Data Cleanup Basics-Identifying Values for Data Cleanup-Formatting Data-Finding Outliers and Bad Data-Finding Duplicates-Fuzzy Matching-RegEx Matching-Normalizing and Standardizing the Data-Saving the Data-Determining suitable Data Cleanup-Scripting the Cleanup- Testing with New Data.

UNIT-4

Data Exploration and Analysis: Exploring Data-Importing Data-Exploring Table Functions-Joining Numerous Datasets-Identifying Correlations-Identifying Outliers-Creating Groupings-Analyzing Data-Separating and Focusing the Data-Presenting Data-Visualizing the Data-Charts-Time-Related Data-Maps- Interactives -Words-Images, Video, and Illustrations-Presentation Tools-Publishing the Data-Open Source Platforms.

UNIT-5

Web Scraping: What to Scrape and How-Analyzing a Web Page-Network/Timeline-Interacting with JavaScript-In-Depth Analysis of a Page-Getting Pages-Reading a Web Page-Reading a Web Page with LXML-XPath-Advanced Web Scraping-Browser-Based Parsing-Screen Reading with Selenium-Screen Reading with Ghost.Py, Spidering the Web-Building a Spider with Scrapy-Crawling Whole Websites with Scrapy.

TEXT BOOKS:

1. Jacqueline Kazil& Katharine Jarmul, “Data Wrangling with Python”, O’Reilly Media, Inc,2016

2. McKinney, William, Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, 2 nd Edition, O’Reilly, 2017

Machine Learning Syllabus

MACHINE LEARNING

UNIT - I

Introduction- Well-Posed Learning Problems, Designing a Learning System, Perspectives and Issues in Machine Learning, Introduction to Supervised, Unsupervised and Reinforcement Learning.

Concept Learning and the General to Specific Ordering – Introduction, A Concept Learning Task, Concept Learning as Search, Find-S: Finding a Maximally Specific Hypothesis, Version Spaces and the Candidate Elimination Algorithm.

UNIT - II

Decision Tree Learning – Introduction, Decision Tree Representation, Appropriate Problems for Decision Tree Learning, The Basic Decision Tree Learning Algorithm, Issues In Decision Tree Learning.

Artificial Neural Networks- Introduction, Neural Network Representation, Appropriate Problems for Neural Network Learning, Perceptrons, Multilayer Networks and the Back-Propagation Algorithm.

UNIT - III

Bayesian Learning – Introduction, Bayes Theorem, Bayes Theorem and Concept Learning, Bayes Optimal Classifier, Naive Bayes Classifier, Bayesian Belief Networks, EM Algorithm.

Instance-Based Learning- Introduction, K-Nearest Neighbor Algorithm, Locally Weighted Regression, Remarks on Lazy and Eager Learning.

UNIT -IV

Genetic Algorithms – Motivation, Genetic Algorithms, An Illustrative Example, Genetic Programming, Models of Evolution and Learning, Parallelizing Genetic Algorithms.

Learning Sets of Rules – Introduction, Sequential Covering Algorithms, Learning Rule Sets: Summary, Learning First-Order Rules, Learning Sets Of First-Order Rules: FOIL

UNIT - V

Analytical Learning- Introduction, Learning With Perfect Domain Theories: PROLOG-EBG, Explanation-Based Learning Of Search Control Knowledge.

Reinforcement Learning – Introduction, The learning task, Q–learning, Nondeterministic, Rewards and Actions, Temporal Difference Learning, Generalizing from Examples, Relationship to Dynamic Programming.

Text Books:

1. Machine Learning – Tom M. Mitchell, – MGH

2. Machine Learning: An Algorithmic Perspective, Stephen Marsland, Taylor & Francis (CRC)

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Machine Learning - Deep Learning