UpdatedSyllabus
GE8151 PROBLEM SOLVING AND PYTHON PROGRAMMING L T P C
3 0 0 3
OBJECTIVES:
To know the basics of algorithmic problem solving
To read and write simple Python programs.
To develop Python programs with conditionals and loops.
To define Python functions and call them.
To use Python data structures –- lists, tuples, dictionaries.
To do input/output with files in Python.
UNIT I ALGORITHMIC PROBLEM SOLVING 9
Algorithms, building blocks of algorithms (statements, state, control flow, functions), notation (pseudo code, flow chart, programming language), algorithmic problem solving, simple strategies for developing algorithms (iteration, recursion). Illustrative problems: find minimum in a list, insert a card in a list of sorted cards, guess an integer number in a range, Towers of Hanoi.
UNIT II DATA, EXPRESSIONS, STATEMENTS 9
Python interpreter and interactive mode; values and types: int, float, boolean, string, and list; variables, expressions, statements, tuple assignment, precedence of operators, comments; modules and functions, function definition and use, flow of execution, parameters and arguments; Illustrative programs: exchange the values of two variables, circulate the values of n variables, distance between two points.
UNIT III CONTROL FLOW, FUNCTIONS 9
Conditionals: Boolean values and operators, conditional (if), alternative (if-else), chained conditional (if-elif-else); Iteration: state, while, for, break, continue, pass; Fruitful functions: return values, parameters, local and global scope, function composition, recursion; Strings: string slices, immutability, string functions and methods, string module; Lists as arrays. Illustrative programs: square root, gcd, exponentiation, sum an array of numbers, linear search, binary search.
UNIT IV LISTS, TUPLES, DICTIONARIES 9
Lists: list operations, list slices, list methods, list loop, mutability, aliasing, cloning lists, list parameters; Tuples: tuple assignment, tuple as return value; Dictionaries: operations and methods; advanced list processing - list comprehension; Illustrative programs: selection sort, insertion sort, mergesort, histogram.
UNIT V FILES, MODULES, PACKAGES 9
Files and exception: text files, reading and writing files, format operator; command line arguments, errors and exceptions, handling exceptions, modules, packages; Illustrative programs: word count, copy file.
OUTCOMES:
Upon completion of the course, students will be able to
Develop algorithmic solutions to simple computational problems
Read, write, execute by hand simple Python programs.
Structure simple Python programs for solving problems.
Decompose a Python program into functions.
Represent compound data using Python lists, tuples, dictionaries.
Read and write data from/to files in Python Programs.
TOTAL : 45 PERIODS
TEXT BOOKS:
Allen B. Downey, ``Think Python: How to Think Like a Computer Scientist‘‘, 2nd edition, Updated for Python 3, Shroff/O‘Reilly Publishers, 2016 Think Python V2 HTML
Guido van Rossum and Fred L. Drake Jr, ―An Introduction to Python – Revised and updated for Python 3.2, Network Theory Ltd., 2011.
REFERENCES:
John V Guttag, ―Introduction to Computation and Programming Using Python‘‘, Revised and expanded Edition, MIT Press , 2013
Robert Sedgewick, Kevin Wayne, Robert Dondero, ―Introduction to Programming in Python:
An Inter-disciplinary Approach, Pearson India Education Services Pvt. Ltd., 2016.
Timothy A. Budd, ―Exploring Python‖, Mc-Graw Hill Education (India) Private Ltd.,, 2015.
Kenneth A. Lambert, ―Fundamentals of Python: First Programs‖, CENGAGE Learning, 2012.
Charles Dierbach, ―Introduction to Computer Science using Python: A Computational Problem- Solving Focus, Wiley India Edition, 2013.
Paul Gries, Jennifer Campbell and Jason Montojo, ―Practical Programming: An Introduction to
Computer Science using Python 3‖, Second edition, Pragmatic Programmers, LLC, 2013.
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