PYTHON PROGRAMMING

Course Description

This course is designed to provide students with a thorough understanding of Python, a leading programming language in the tech industry. The course aims to equip students with advanced programming skills through a structured yet flexible curriculum. Students will cover core topics including python for data analysis, AI, scripting, automation, software development while engaging in hands-on projects that simulate real-world applications. The course emphasizes critical thinking, problem-solving, and coding best practices. As students progress, they will develop the ability to write efficient and effective Python code. By completion of the course, students will have gained practical experience and confidence in their programming abilities, preparing them for advanced studies or professional opportunities in the industry. Completion of the course requires a final project, which students will present in a comprehensive oral presentation and submit as a formal report.

PYTHON PROGRAMMING

Evaluation Tools (Active Term)

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Course outcomes

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Course Syllabus

Reference Books & Course Materials

1. 01 Python for data analysis: Data wrangling with pandas, numpy, and jupyter. McKinney, W. (2022), O'Reilly Media, Inc.
2. 02 Automate the boring stuff with Python, Sweigart, A. (2025).
3. 03 Flask Web Development: Developing Web Applications with Python. 2018. Grinberg, M URL http://flaskbook. com.

Learning Outcomes

1. L01 Demonstrate and apply coding best practices to produce clean, efficient, and well-documented Python code. SOLO 4
2. L02 Apply and implement Python-based solutions to real-world problems in automation, scripting, and software development. SOLO 4
3. L03 Implement and analyze data using Python-based techniques with core libraries such as NumPy, pandas, and Matplotlib. SOLO 4
4. L04 Examine and describe foundational concepts in artificial intelligence using Python libraries such as scikit-learn or TensorFlow. SOLO 3
5. L05 Design and develop modular Python programs by synthesizing functions, classes, and appropriate data structures SOLO 5
6. L06 Apply and execute Python tools to automate tasks and workflows, including file handling, scheduling, and command-line operations. SOLO 3.5
7. L07 Analyze and review Python code using testing techniques and performance evaluation tools to identify and resolve errors. SOLO 4
8. L08 Perform and conduct collaborative work within team-based programming projects that simulate professional development environments.. SOLO 3.5
9. L09 Explain and summarize the results of programming projects, relating findings to project objectives, through structured oral presentations and written reports SOLO 3.5

Program Outcomes

1. P01 Apply data science principles and techniques to challenges in real life situations, and effectively communicate their solutions.
2. P02 Identify and implement data analysis methodologies based on theoretical ideas, ethical code, and in-depth knowledge of the underlying data.
3. P03 Analyze the guiding concepts and assessment procedures for information analysis in real-life applications.
4. P04 Design and apply relevant data analysis models to find obscure solutions to business-related problems.
5. P05 Utilize modern computing techniques to handle real-world problems characterized by massive amounts of data, such as parallel and distributed computing and machine learning.
6. P06 Configure and administer the software tools required to efficiently produce usable information from any size of structured and unstructured datasets.
7. P07 Administer or manage data science tools and techniques to organize and complete projects aimed at gaining useful insight from complex data.
8. P08 Think critically and imaginatively, conceiving real-world issues from several angles, and work well in a variety of teams to solve issues cooperatively.
9. P09 Be able to effectively integrate data‐based solutions into the user environment and help non-technical professionals in exploring, visualizing, and using these solutions
10. P10 Understand their obligations under professional and ethical standards in relation to matters like data ownership and citation, data security and sensitivity and the privacy implications of data analysis.

Po-Lo Matrix