Embedded Files
Course Learning Objective
Course Learning Objective
To provide students with the skills and knowledge to use Python for solving complex computational problems in science and engineering, fostering critical thinking, analytical reasoning, and collaborative skills.
Course Outcomes:
Course Outcomes:
By the end of this course, students will be able to:
Analyze scientific problems and develop Python-based solutions (Complex problem-solving, Analytical reasoning).
Evaluate the efficiency and effectiveness of Python programs in solving computational problems (Critical Thinking, Research-related skills).
Design and implement Python programs for various scientific applications using appropriate libraries and tools (Digital Literacy, Self-Directed Learning).
Collaborate effectively in teams to develop and optimize Python-based solutions, demonstrating leadership and teamwork skills (Cooperative/Teamwork, Leadership readiness).
Communicate technical concepts and solutions related to Python programming clearly and effectively (Communication Skills, Multicultural competence and inclusive spirit).
Textbooks
Textbooks
Syllabus
Syllabus
Unit 1: Introduction to Python and Computational Science (9 hours)
Unit 1: Introduction to Python and Computational Science (9 hours)
Overview of Python programming language
Python installation and setup
Basic syntax and data types
Control structures: loops and conditionals
Introduction to computational science and its applications
Unit 2: Scientific Computing with Python (9 hours)
Unit 2: Scientific Computing with Python (9 hours)
NumPy for numerical computations
SciPy for scientific computing
Matplotlib for data visualization
Pandas for data manipulation and analysis
Case studies in scientific computing
Unit 3: Advanced Python Programming (9 hours)
Unit 3: Advanced Python Programming (9 hours)
Object-oriented programming in Python
File handling and data processing
Error handling and debugging
Performance optimization techniques
Introduction to parallel computing with Python
Unit 4: Python for Data Analysis and Visualization (9 hours)
Unit 4: Python for Data Analysis and Visualization (9 hours)
Data analysis with Pandas
Data visualization with Matplotlib and Seaborn
Statistical analysis and hypothesis testing
Machine learning basics with Scikit-learn
Real-world data analysis projects
Unit 5: Applications of Python in Computational Science (9 hours)
Unit 5: Applications of Python in Computational Science (9 hours)
Bioinformatics and computational biology
Computational physics and chemistry
Environmental modeling and simulation
Big data and cloud computing with Python
Future trends and research directions in computational science
Lab Exercises
Lab Exercises
Exercises:
Exercises:
Write a Python program to solve a basic mathematical problem.
Implement a numerical computation using NumPy.
Create a data visualization using Matplotlib.
Analyze a dataset using Pandas and present the findings.
Develop a Python script for file handling and data processing.
Implement object-oriented programming concepts in a Python project.
Debug and optimize a Python program for better performance.
Perform a statistical analysis on a given dataset.
Develop a simple machine learning model using Scikit-learn.
Implement a parallel computing task using Python.
Create a data visualization project using Seaborn.
Develop a Python program for a bioinformatics application.
Simulate a physical system using Python.
Model an environmental process using Python.
Analyze big data using Python and cloud computing tools.
Collaborate on a group project to solve a computational science problem.
Write a report on the application of Python in a specific scientific domain.
Present a case study on the use of Python in computational science.
Research and present on the future trends in Python for computational science.
Participate in a debate on the ethical implications of computational science.
Page updated
Google Sites
Report abuse