Python Programming. Unit III

Transcription

1 Python Programming Course learning objectives 1. To acquire programming skills in core Python. 2. To acquire Object Oriented Skills in Python 3. To develop the skill of designing Graphical user Interfaces in Python 4. To develop the ability to write database applications in Python Pre-requisites : Computer Concepts and C Programming, Database Management Systems Unit I Introduction to Python, use IDLE to develop programs, Basic coding skills, working with data types and variables, working with numeric data, working with string data, Python functions, Boolean expressions, selection structure, iteration structure, Illustrative Programs, Exercises Unit II Define and use functions and modules, working with recursion, Basic skills for working with lists, work with a list of lists, work with tuples, work with dates and times, get started with dictionaries, Illustrative programs, Exercises. Unit III 6 Hours An introduction to file I/O, use text files, use CSV files, use binary files, Handle a single exception, handle multiple exceptions, Illustrative programs, Exercises 9 Hours Unit IV Object Oriented Programming, An introduction to classes and objects, define a class, work with object composition, work with encapsulation, work with inheritance, override object methods, Illustrative programs, Exercises Unit V An introduction to relational databases, SQL statements for data manipulation, Using SQLite Manager to work with a database, Using Python to work with a database, Creating a GUI that handles an event, working with components, Illustrative programs, Exercises Books 1. Michael Urban and Joel Murach, Python Programming, Shroff/Murach, 2016 Reference Book 1. Mark Lutz, Programming Python, O`Reilly, 4 th Edition, 2010

2 Course Outcomes (COs) At the end of the course, the student will be able to Bloom s Level 1. Explain basic principles of Python programming language L2 2. Implement object oriented concepts, L3 3. Implement database and GUI applications. L Program Outcome of this course (POs) Design/development of solutions: Design solutions for complex engineering problems and design system components or processes that meet the specified needs with appropriate consideration for the public health and safety, and the cultural, societal, and environmental considerations. Modern tool usage: Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction and modeling to complex engineering activities with an understanding of the limitations. Life-long learning: Recognize the need for, and have the preparation and ability to engage in independent and life-long learning in the broadest context of technological change. PO No. PO3 PO5 PO12

3 Unix and Shell Programming Course learning objectives 1. To provide introduction to UNIX operating system and its File System. 2. To develop the ability to formulate Regular Expressions and use them for Pattern Matching. 3. To gain an understanding of important aspects related to the Shell and the Process. 4. To provide a comprehensive introduction to Shell Programming, Services and Utilities. Pre-requisites: Basic knowledge of computer concepts & programming. Unit I Introduction to UNIX and File-system Inside UNIX, General features of a command, PATH, Internal and External commands, Command structure, General purpose utilities. The File System: The File, What s in a (File)name, The Parent-Child relationship, The UNIX File System, pwd, Absolute pathnames, cd, Relative pathnames, mkdir, rmdir, cp, rm, mv, cat, file, File Attributes: ls, ls -l, ls -d, File Permissions, chmod. Unit II 9 Hours vi/vim Editor vi Preliminaries,Quitting vi The Last Line Mode, Inserting and Replacing Text, Saving Text(:w), Exit to the UNIX Shell, The Repeat Factor, The Command Mode, Navigation, Operators, Deleting, Moving and Yanking Text, Changing Text ( c and - ), The Dot: Repeating the Last Command, Undoing Last Editing Instructions ( u and U), String Search, Searching with Regular Expressions, Search and Replace (:s), Unit III Shell and Process: The Shell: The shell as command processor, Pattern matching, Redirection, Pipes, tee, Command substitution. The Process: Understanding the Process, How a process is created, The Login shell, init, Internal and External commands, ps, running jobs in background, nice, Signals, kill. Unit IV 7 Hours Filters :SimpleFilters:wc,cmp,diff,head,tail,sort, Filters using Regular Expressions: grep:searching for pattern,grep options,regular expressions- Round one,egrep and fgrep: The other members,regular expressions- Round two,sed: The Stream editor,line addressing,context addressing. Shell Programming: Unit V Shell variables, Shell Scripts, read: Making scripts interactive, Positional parameters, Exit status of command, Logical operators && and - Conditional execution, exit: script termination, the if conditional, test and [ ], the case conditional, expr: Computation and String handling, sleep and wait, while and until : Looping, for: Looping with a list. Text Books 1. Sumitabha Das: YOUR UNIX The Ultimate Guide, Tata McGraw Hill, 23 rd reprint, 2012 and onwards. 2. Sumitabha Das: UNIX Concepts and Applications, 4 th Edition, Tata McGraw Hill, Copyright 2006 and onwards.

4 Reference Books 1. Behrouz A. Forouzan and Richard F. Gilberg: UNIX and Shell Programming, Cengage Learning, 2005 and onwards. 2. M.G. Venkateshmurthy: UNIX & Shell Programming, Pearson Education, 2005and onwards. Course Outcome (COs) At the end of the course, the student will be able to Bloom s Level 1. Describe the architecture and features of the UNIX operating system and distinguish it from other operating systems. L1, L2 2. Demonstrate UNIX commands for file handling and process control L3 3. Construct regular expressions for pattern matching and apply them to various filters for a specific task. L3 4. Analyze a given problem and apply requisite facets of shell programming in order to devise a shell script to solve the problem L4 Program Outcome of this course (POs) 1. Engineering knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to the solution of complex engineering problems 2. Design/development of solutions: Design solutions for complex engineering problems and design system components or processes that meet the specified needs with appropriate consideration for the public health and safety, and the cultural, societal, and environmental considerations. 3. Life-long learning: recognizing the need for, and have the preparation and ability to engage in independent and life-long learning in the broadest context of technological change. PO No

5 Object Oriented Programming with JAVA Course learning objectives (CLOs): 1. Gain knowledge about basic Java language syntax and semantics to write Java programs and use concepts such as variables, conditional and iterative execution methods etc. 2. Understand the fundamentals of object-oriented programming in Java, including defining classes, objects, invoking methods etc and exception handling mechanisms. 3. Understand the principles of inheritance, packages and interfaces. Prerequisites: C Programming Concepts. Unit I OOP PARADIGM: Object-oriented programming, two paradigms, three OOP principles, overriding and exceptions, abstraction mechanisms. JAVA BASICS: History of Java, Java buzzwords, type conversion and casting. Java s Magic: The Byte code. Unit II INTRODUCING CLASSES : Class Fundamentals, The General Form of a Class, A Simple Class, Declaring Objects, A Closer Look at new, Assigning Object Reference Variables, Introducing Methods, Adding a Method to the Box Class, Returning a Value, Adding a Method That Takes Parameters, Constructors, Parameterized Constructors, The this Keyword, Instance Variable Hiding, Garbage Collection, The finalize( ) Method. Unit III A Closer Look at Methods and Classes: Overloading Methods, Overloading Constructors, Using Objects as Parameters, A Closer Look at Argument Passing, Returning Objects, Introducing Access Control, Understanding static, Introducing final, Introducing Nested and Inner Classes. Unit IV Inheritance: Hierarchical abstractions, Base class object, subclass, subtype, substitutability, forms of inheritance- specialization, specification, construction, extension, limitation, combination, benefits of inheritance, costs of inheritance. Member access rules, super uses, using final with inheritance, polymorphism, method overriding, abstract classes, the Object class. Unit V Exception handling: Concepts of exception handling, benefits of exception handling, exception hierarchy, usage of try, catch, throw, throws and finally, built in exceptions, creating own exception sub classes, Packages and interfaces.

6 Text Books: 1. Herbert Schildt, Java the Complete Reference, TMH. 8th edition. 2. Kathy Sierra & Bert Bates, Head First Java, O Reilly, 2 nd Edition. Reference Books: 1. E Balagurusamy, Programming with Java A Primer, TMH, 4th edition. 2. Patrick Naughton, Java Handbook, Osborne McGraw-Hill Course Outcome (Cos) At the end of the course, the student will be able to Bloom s Level 1. Identify classes, objects, members of a class and relationships among them L4 needed for a specific problem 2. Write Java application programs using OOP principles and proper program L3 structuring 3. Demonstrate the concepts of polymorphism and inheritance L2 4. Write Java programs to implement error handling techniques using exception handling L3 Program Outcome of this course (POs) 1. Engineering knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to the solution of complex engineering problems. 2. Conduct investigations of complex problems: Use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions. 3. Life-long learning: Recognize the need for, and have the preparation and ability to engage in independent and life-long learning in the broadest context of technological change. PO No. PO1 PO4 PO12

7 Data Structures Course learning objectives(clos): 1. To bring out the importance of data structures in a variety of applications. 2. To introduce linear (arrays, linked list, doubly linked list) and non linear data structures (Binary Tree, Heap). 3. To present the advantages and applications of hashing. Prerequisites: Basic computer concepts & C programming. Unit I Basic Concepts: Pointers and Dynamic Memory Allocation, Recursion, Arrays, Dynamically Allocated Arrays, Structures and Unions Unit II 7 Hours Stacks and queues: Stacks, Queues, Evaluation of Expressions. Unit III Linked lists: Singly Linked lists and Chains, Representing Chains in C, Additional List operations, Circular Linked Lists. Unit IV Trees: Introduction, binary trees, binary tree traversals, heaps, binary search trees. Unit V 7 Hours Hashing: Introduction, Hashing methods, Collision Resolution Techniques. Text Book: 1. Horowitz, Sahni, Anderson-Freed: Fundamentals of Data Structures in C, 2nd Edition, Universities Press, 2007 and onwards. 2. Data Structures: A Pseudocode Approach with C by Richard.F.Gilberg, Behrouz.A.Forouzan, 2nd edition 2007 and onwards. Reference Books: 1. Yedidyah, Augenstein, Tannenbaum: Data Structures Using C and C++, 2nd Edition, Pearson Education, 2003 and onwards. 2. Debasis Samanta: Classic Data Structures, 2nd Edition, PHI, 2009 and onwards. Course Outcome (Cos) At the end of the course, the student will be able to Bloom s

8 Level 1. Identify the appropriate and optimal data structure for a specified application. L1 2. Employ the benefits of dynamic and static data structures implementations L3 3. Illustrate the use of different non-linear data structures and their applications. L3 4. Demonstrate the use of techniques like hashing, trees and heaps in a variety of applications. L3 Program Outcome of this course (POs) 1. Problem analysis: Identify, formulate, review research literature, and analyze complex engineering problems reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering sciences. 2. Design/development of solutions: Design solutions for complex engineering problems and design system components or processes that meet the specified needs with appropriate consideration for the public health and safety, and the cultural, societal, and environmental considerations. 3. Life-long learning: Recognize the need for, and have the preparation and ability to engage in independent and life-long learning in the broadest context of technological change. PO No. PO2 PO3 PO12

9 DATABASE MANAGEMENT SYSTEM Course learning objectives 1. To discuss and realize the importance of Database Architecture Design notations, ER Modeling, Mapping and Schema design. 2. To gain the knowledge Relational algebra and learn the use of SQL and PL/SQL. 3. To introduce formal database design approach through normalization and discuss various normal forms. 4. To understand the importance of Concurrent Transactions and discuss issues and transaction control algorithms. Pre-requisites : Basic programming concepts. Unit I Introduction: Introduction to database, Characteristics of Database approach, Advantages of using DBMS approach, When not to use a DBMS; Actors on the scene, Workers behind the scene; Three-schema architecture and data independence. Unit II 7 Hours Entity-Relationship Model: Using High-Level Conceptual Data Models for Database Design; An Example Database Application; Entity Types, Entity Sets, Attributes and Keys; Relationships, Relationship types, Roles and Structural Constraints; Weak Entity Types. Unit III Relational Model and Relational Algebra: Relational Model Concepts; Relational Model Constraints and Relational Database Schemas; Update Operations, Dealing with constraint violations; Unary Relational Operations: SELECT and PROJECT; Relational Algebra Operations from Set Theory; Binary Relational Operations: JOIN and DIVISION; Unit IV Database Design: Informal Design Guidelines for Relation Schemas; Functional Dependencies; Normal Forms Based on Primary Keys; General Definitions of Second and Third Normal Forms. Unit V 9 Hours SQL : SQL Data Definition and Data Types; Specifying basic constraints in SQL; Schema change statements in SQL; Basic queries in SQL; More complex SQL Queries. Insert, Delete and Update statements in SQL. TEXT BOOKS: 1. Elmasri and Navathe: Fundamentals of Database Systems, 5 th Edition, Addison-Wesley, 2007 (Chapter No. 1, 2, 3, 5, 6, 7.1, 8, 10)

10 2. Raghu Ramakrishnan and Johannes Gehrke: Database Management Systems, 3 rd Edition, McGraw-Hill, (Chapter No. 16) REFERENCE BOOKS: 1. Silberschatz, Korth and Sudharshan: Data base System Concepts, 5th Edition, Mc- GrawHill, C.J. Date, A. Kannan, S. Swamynatham: A Introduction to Database Systems, 8th Edition, Pearson education, Course Outcome (COs): 1. Apply the database concepts and design database for given information system.[l 3] 2. Create database and develop database programming skills in SQL and PL/SQL.[L 5] 3. Apply the concepts of Normalization and design database which possess no anomalies.[l 3] 4. Explain the issues of transaction like concurrency control, recovery and security. [L 2] Program Outcomes (POs) of the course: 1. Graduates will demonstrate the ability to identify, formulate and solve computer systems engineering problems. [PO2] 2. Graduates will demonstrate the ability to design and experiment both in hardware and software, analyze and interpret data. [PO3] 3. Graduates will demonstrate an ability to analyze the given problems and design solutions, as per the needs and specifications. [PO4] 4. Graduates will develop confidence for self education and ability for lifelong learning [PO10] 5. Graduate will be capable of participating and succeeding in competitive examinations. [PO11]

11 Machine Learning Course Learning Objectives 1. To introduce the concept of machine learning. 2. To present various Machine Learning algorithms and their applications 3. To show to the implementation of Machine Learning techniques with hands on sessions Pre-requisites: Algorithms, Probability theory Unit I 5+5* Hours Introduction, Concept Learning and Decision Trees Learning Problems Designing Learning systems, Perspectives and Issues Concept Learning Version Spaces and Candidate Elimination Algorithm Inductive bias Decision Tree learning Representation Algorithm Heuristic Space Search Unit II 5+5* Hours Neural Networks Neural Network Representation Problems Perceptrons Multilayer Networks and Back Propagation Algorithm. Unit III 5+5* Hours Bayesian and Computational Learning Baye s Theorem Concept Learning Maximum Likelihood Minimum Description Length principle Bayes Optimal Classifier Baysian Belief Network. Unit IV 5+5* Hours Instance based Learning and Learning set of rules K-nearest neighbor learning Locally weighted regression radial basis functions case based reasoning, SVM Classifier. Text Book: 1. Tom M. Mitchell, Machine Learning, McGraw-Hill Education (INDIAN EDITION), Reference Books: 1. Ethem Alpaydin, Introduction to Machine Learning, 2 nd Edition, PHI Learning Pvt. Ltd., T Hastie, R. Tibshirani, J.H.Fiedman, The Elements of statistical learning, Springer, 1 st Edition 2001.

12 Course Outcome (Cos) At the end of the course, the student will be able to 1. Demonstrate the implementation of Machine Learning Techniques using tools such as Weka, R and Matlab.[ 2. Identify and apply the appropriate machine learning technique for classification, pattern recognition, optimization and decision problems Bloom s Level L2 L3 Program Outcome of this course (POs) 1 Engineering knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to the solution of complex engineering problems. 2 Problem analysis: Identify, formulate, review research literature, and analyze complex engineering problems reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering sciences. 3 Modern tool usage: Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction and modeling to complex engineering activities with an understanding of the limitations. 4 Individual and team work: Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings. 5 Communication: Communicate effectively on complex engineering activities with the engineering community and with society at large, such as, being able to comprehend and write effective reports and design documentation, make effective presentations, and give and receive clear instructions. PO No

13 BIG DATA & HADOOP Course learning objectives 1. To understand Big data dimensions and its applications with case studies. 2. To explore Hadoop framework and architecture 3. To understand basics of NoSQL 4. To understand the importance of MapReduce framework 5. To explore Big Data Tools and Technologies : Pig and Hive Pre-requisites : Database Management System, Unix Shell Programming Unit I Introduction: Big Data Definition, History of Data Management-Evolution of Big Data, Structuring Big Data, Elements of Big Data, Big Data Analytics, Careers in Big Data, Future of Big Data, Use of Big Data in Social Networking, Use of Big Data in Preventing Fraudulent Activities; Use of Big Data in Retail Industry Unit II Hadoop Ecosystem: Understanding Hadoop Ecosystem, Hadoop Distributed File System:HDFS Architecture,Concept of Blocks in HDFS Architecture, NameNodes and Data Nodes, The Command-Line Interface, Using HDFS Files, Hadoop-Specific File System Types, HDFS Commands, The org.apache.hadoop.io package,hdfs High availability:features of HDFS. Unit III NoSQL: Introduction to NoSQL: Why NoSQL, Characteristics of NoSQL, History of NoSQL, Types of NoSQL Data Models: Key-Value Data Model, Column-Oriented Data Model, Document Data Model, Graph Databases, Schemaless Databases, Materialized views, Distribution Models: CAP Theorem, Sharding. Unit IV Understanding MapReduce: The MapReduce Framework: Exploring the Features of MapReduce,Working of MapReduce, Exploring Map and Reduce Functions, Uses of MapReduce. YARN Architecture: Background; Advantages of YARN;YARN Architecture

14 Unit V Properties of Pig : Introducing Hive, Getting started with Hive, Hive Services, Data types in Hive, Built-in Functions in Hive Analyzing Data with Pig: Introduction to Pig: The Pig Architecture, Benefits of Pig, Properties of Pig Text Book: 1. DT Editorial Services, Big Data:Black Book,Comprehensive Problem Solver, Dreamtech Press Edition [ Chapters - 1,2,4,5,11,12,13,15] Reference Book: 1. Paul C. Zikopoulos, Chris Eaton, Dirk deroos, Thomas Deutsch, George Lapis, Understanding Big Data Analytics for Enterprise Class Hadoop and Streaming Data, McGraw Hill, P. J. Sadalage and M. Fowler, "NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence", Addison-Wesley Professional, Tom White, "Hadoop: The Definitive Guide", Third Edition, O'Reilly, Course Outcome (COs) At the end of the course, the student will be able to Bloom s Level 1. Outline the importance of Big Data, its characteristics and use of Big L1 Data in different fields/sectors. 2. Explain the ecosystem of Hadoop L3 3. Infer why to use NoSQL databases in the Big Data Scenario. L2 4. Apply map reduce framework in analyzing the data and relate to YARN L3 5. Explain tools in analyzing the data and managing Big Data L2 Program Outcome of this course (POs) 1. Engineering knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to the solution of complex engineering problems. 2. Problem analysis: Identify, formulate, review research literature, and analyze complex engineering problems reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering sciences. 3. Design/development of solutions: Design solutions for complex engineering problems and design system components or processes that meet the specified needs with appropriate consideration for the public health and safety, and the cultural, societal, and environmental considerations. PO No

15 Data Mining Course Learning Objectives 1. To introduce the fundamental concepts of data mining and Recognize various types of data mining tasks. 2. To introduce mathematical and statistical models used in data Classification. 3. To define, understand and interpret association rules. 4. Discuss the clustering algorithms to solve real-world problems. Pre-requisites : Data Base Management Systems, Design and Analysis of Algorithms Unit I 7 Hours Data Mining: Introduction, Challenges, Data Mining Tasks, Types of Data, Data Preprocessing, Measures of Similarity and Dissimilarity, Data Mining Applications, Using WEKA Software. Unit II 7 Hours Classification-1 : Basics, General approach to solve classification problem, Decision Tree Induction, Rule Based Classifier, Nearest Neighbor Classifier. Unit III Classification-2 : Bayesian Classifiers, Artificial Neural Network, Support Vector Machine, Estimating Predictive accuracy of classification methods, Improving accuracy of classification methods, Evaluation criteria for classification methods. Unit IV Association Analysis: Basic Concepts and Algorithms: Frequent Itemset Generation, Rule Generation, Compact Representation of Frequent Itemsets, Alternative Methods for Generating Frequent Itemsets, FP-Growth Algorithm, Evaluation of Association Patterns. Unit V Cluster Analysis: Overview, K-means, Agglomerative Hierarchical Clustering, DBSCAN, Cluster Evaluation, Characteristics of Data, Clusters, and Clustering Algorithms. Books 1. Pang-Ning Tan, Michael Steinbach, Vipin Kumar: Introduction to Data Mining, Addison-Wesley, G. K. Gupta: Introduction to Data Mining with Case Studies, 3rd Edition, PHI, New Delhi, Reference Books 1. Jiawei Han, Micheline Kamber, Jian Pei: Data Mining - Concepts and Techniques, 3rd Edition, Morgan Kaufmann Publishers, 2011.

16 Course Outcomes (COs) At the end of the course, the student will be able to Bloom s Level 1. Explain the basic steps in data-mining. L2 2. Classify data by applying various clustering algorithms. L3 3. Evaluate the performance of various Classification algorithms. L5 4. Recognize interesting patterns from large amounts of data to analyze and extract patterns to make predictions of outcomes. 5. Illustrate the application of clustering algorithms to real-world problems. L4 L Program Outcome of this course (POs) Engineering knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to the solution of complex engineering problems. Conduct investigations of complex problems: Use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions. Modern tool usage: Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction and modelling to complex engineering activities with an understanding of the limitations. Life-long learning: Recognize the need for, and have the preparation and ability to engage in independent and life-long learning in the broadest context of technological change. PO No