1 Course Outline
1.1 Course Overview
Credits: 2
Hours per Week: 2
Course Objectives:
- To develop algorithmic thinking and problem-solving skills.
- To familiarize students with computer systems, software, and language translators.
- To justify the use of Python for programming and algorithmic design.
- To introduce Python programming concepts, including data types, conditional statements, and loops.
- To implement functions, string operations, and real-time applications using Python’s data structures.
- To enable students to apply their knowledge to solve practical problems through a micro project.
1.2 Key Ideas
1.2.1 Topic 1: Introduction to Algorithmic Thinking
Lesson Outcomes:
Understand the concept of algorithmic thinking.
Develop basic algorithms for simple problems.
Recognize the importance of algorithms in problem-solving.
Content:
What is Algorithmic Thinking?
Importance in problem-solving
Steps in designing an algorithm
Example Algorithms:
- Simple tasks (e.g., making a cup of tea)
Practical Situation:
- Create algorithms for everyday activities to illustrate the concept.
1.2.2 Topic 2: Familiarization with Computer Systems and Language Translators
Lesson Outcomes:
Understand basic computer architecture and components.
Identify different types of software and language translators.
Content:
Introduction to Computer Architecture:
Block diagram of a computer
Hardware components (Input, Output devices)
Memory types
Software Types:
High-level vs. Low-level languages
Assembly languages
Language Translators:
- Compilers, Interpreters, Assemblers
Practical Situation:
- Explore how different programming languages and translators affect the execution of a simple program.
1.2.3 Topic 3: Justification for Using Python
Lesson Outcomes:
Understand the advantages of Python for algorithmic thinking and programming.
Compare Python with other programming languages in terms of simplicity and effectiveness.
Content:
Why Python?
Python’s simplicity and readability
Comparison with other languages (e.g., C++, Java)
Python’s role in modern software development and data science
Practical Situation:
- Demonstrate a basic Python script and compare it with an equivalent script in another language.
1.2.4 Topic 4: Developing Algorithms and Flow Charts
Lesson Outcomes:
Develop and represent algorithms using flowcharts.
Understand properties of good algorithms.
Content:
Introduction to Algorithms:
- Properties of good algorithms
Flowchart Creation:
- Basic flowchart symbols and conventions
Practical Situation:
- Design flowcharts for simple algorithms (e.g., sorting a list of numbers).
1.2.5 Topic 5: Data Types and Arithmetic Operations in Python
Lesson Outcomes:
Understand and use basic data types and operators in Python.
Perform arithmetic operations and handle expressions.
Content: - Introduction to Python Programming:
Data types (int, float, str, etc.)
Keywords and Variables
Input and Output statements
Operators and Arithmetic expressions
Operator precedence and Evaluation of expressions
Practical Situation:
- Write a Python program that performs various arithmetic operations and displays results.
1.2.6 Topic 6: Conditional Statements in Python
Lesson Outcomes:
- Implement and use conditional statements to control the flow of programs.
Content: - Types of Conditional Statements:
if,if-else,elif, nestedif-else,if-elif-elsePractical Examples:
- Programs using conditional statements
Practical Situation:
- Create a Python program that determines if a number is positive, negative, or zero.
1.2.7 Topic 7: Loop Structures in Python
Lesson Outcomes:
- Use loop structures to repeat actions and iterate over data.
Content:
Introduction to Looping:
for,while, nested loopsbreak,continue,passstatementsrangefunction
Sample Programs:
- Implementing various loop constructs
Practical Situation:
- Write a Python program that calculates the factorial of a number using loops.
1.2.8 Topic 8: Functions in Python
Lesson Outcomes:
Define and use functions for modular programming.
Understand function concepts including parameter passing and return values.
Content:
Concept of Functions:
Definition, Calling Functions
Passing Parameters and Return Values
Type Conversion and Coercion
Advanced Function Concepts:
Lambda functions
Built-in Mathematical functions
Sample Programs Using Functions
Practical Situation:
- Develop a Python program that uses functions to perform mathematical operations.
1.2.9 Topic 9: String Operations in Python
Lesson Outcomes:
- Manipulate and process strings using Python’s string handling functions.
Content:
Introduction to Strings:
- String creation and manipulation
String Handling Functions:
- Commonly used functions (e.g.,
split(),join(),replace())
- Commonly used functions (e.g.,
Practical Situation:
- Write a Python program that processes and formats user input strings.
1.2.10 Topic 10: Real-time/Technical Applications Using Data Structures
Lesson Outcomes:
- Apply data structures (lists, tuples, dictionaries) to solve real-world problems.
Content:
Lists, Tuples, Dictionaries:
Concepts, operations, and functions
Mutable vs Immutable data structures
Applications:
Identifying use cases
Solving problems using lists, tuples, and dictionaries
Practical Situation:
- Create a Python program that manages a list of student records using lists and dictionaries.
1.2.11 Topic 11: Micro Project
Lesson Outcomes:
- Apply Python concepts to develop a project relevant to the student’s field of study.
Content:
Project Development:
Design and implementation of a simple project
Application of learned concepts to a practical problem
Practical Situation:
- Develop and present a micro project related to the student’s branch of study.
1.3 Teaching Methodology
Hands-on lab exercises
Step-by-step problem-solving approach
Regular assessments and feedback
1.4 Evaluation
Lab exercises and practical implementation
Micro Project