Programming Language | MIT Syllabus | TU

Programming Language

Course Title: Programming Language                                                         Full Marks: 45+30

Course No: MIT505                                                                                      Pass Marks: 22.5+15

Nature of the Course: Theory + Practical                                                    Credit Hrs: 3

Semester: I

Course Description:

This course covers basics and of procedural and object oriented aspects of python programming language and also covers detailed discussion on using various libraries their applications in various data processing tasks.

Course Objectives:

The main objective of this course is to provide knowledge of procedural and object oriented programming using python programming and apply it in data processing tasks.

Course Contents:

Unit 1: Procedural Python (12 Hrs.)

Tokens, Reserved Words, Identifier, Data types, variables and Constants, Literals, Operators, Operator Precedence, Escape sequences, Numbers, Comments, Control Flow: Conditional statements, Ternary operator, Loops, Jump statements. Functions: Defining and Calling Functions, Passing Arguments, Returning values, Global and Local variables, Recursive functions, anonymous functions, Lambda expressions. Strings: String Functions, String Concatenation, String operations, String slicing, string formatters; Working with Lists, Tuples, Sets, and Dictionaries; Functions, methods, and operations of each data structure

Unit 2: Object Oriented Python (10 Hrs.)

Class, Object, constrictors, access modifiers, static methods, method overloading, operator overloading, inheritance, method overriding, abstract classes; Enumerations, Exception Handling, File Handling, Regular Expressions

Unit 3: Libraries (10 Hrs.)

NumPy: NumPy Basics, Array and vectorized processing, operations between arrays and scalars, slicing and indexing, multi-dimensional array, data processing with arrays, array object, array functions, File input and output with arrays, Linear Algebra with arrays, random number generation; Pandas: Pandas Data structure, Essential Functionalities, Summarizing and Computing Descriptive Statistics, Handling Missing Data, Hierarchical Indexing; Matplotlib: Introduction, Plotting Functions in pandas, Plotting Maps, Python Visualization Tool Ecosystem

Unit 4: Data Processing (13 Hrs.)

Data Loading, Storage, and File Formats: Reading and Writing Data in Text Format, Reading and Writing Data in Text Format, Binary Data Formats, Interacting with HTML and Web APIs, Interacting with Databases; Data Wrangling: Combining and Merging Data Sets, Reshaping and Pivoting, Data Transformation; Data Aggregation and Group Operations: GroupBy Mechanics, Data Aggregation, Group-wise operations and Transformations, Pivot Tables and Cross-Tabulation Laboratory Works:

Students need to write python programs using procedural as well as object oriented approach. Besides, they need to use various libraries discussed in the class and solve various data processing problems


  1. AMZ Press, Python Programming for Beginners: The Ultimate Guide for Beginners to Learn Python Programming: Crash Course on Python Programming for Beginners, Independently published, First Edition, 2022
  2. Abhishek Singh, Master Python Programming: Learn Python like Never Before, ieepepeepedni published, First Edition, 2022
  3. William McKinney, Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, 2 ,apinni lpeiR’O nd Edition, 2017
  4. Daniel Zingaro, Learn to Code by Solving Problems: A Python Programming Primer, No Starch Press, First Edition, 2021

Codeone Publishing, Python Programming for Beginners: The #1 Python Programming Crash Course to Learn

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