INTRODUCTION TO PROGRAMMING

Course Description

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INTRODUCTION TO PROGRAMMING

Evaluation Tools (Active Term)

Course outcomes

1. 01 analyse problems and design solutions through algorithms (draw flowchart and write pseoudocode);
2. 02 define fundamental constructs of computer programming (i.e. sequence, selection and loops);
3. 03 convert algorithms into C++ compatible programming code;
4. 04 write structured programs using user defined functions;
5. 05 write structured programs using arrays;
6. 06 Identify programming constructs from given problems and create structured programming patterns when writing computer programs.

Course Syllabus

Reference Books & Course Materials

1. 01 Introducing Python: Modern Computing in Simple Packages, Bill Lubanovic, O'Reilly, 2019
2. 02 Python Programming for Beginners, Philip Robbins, Functional Software Programming, 2024

Learning Outcomes

1. L01 analyse problems and design solutions through algorithms (draw flowchart and write pseoudocode);
2. L02 define fundamental constructs of computer programming (i.e. sequence, selection and loops);
3. L03 convert algorithms into C++ compatible programming code;
4. L04 write structured programs using user defined functions;
5. L05 write structured programs using arrays;
6. L06 Identify programming constructs from given problems and create structured programming patterns when writing computer programs.

Program Outcomes

1. Demonstrate a thorough understanding of the theories, frameworks, and models in order to assess and comprehend the state of the art in data science.
2. Review the literature and apply data science theories and methodology in new research and experiments.
3. Analyze datasets using supervised and unsupervised machine learning techniques.
4. Design, develop and test statistics and informatics software systems for data management, analysis and problem solving.
5. Conceptualize and develop efficient visuals for a range of data types and analytical tasks, and carry out independent research on a range of theoretical and applied subjects in visualization and visual analytics.
6. Obtain a high level of proficiency in communication, problem solving, research or project-related activities and function effectively as a team member or a leader to accomplish a common goal in a multidisciplinary team.
7. Develop and implement optimal solutions to overcome challenges associated with managing large datasets by utilizing parallel methods, cloud computing, and non-relational data storage and retrieval (NoSQL).
8. Demonstrate an understanding of the interdisciplinary of data, information, and communications, as well as the ability to evaluate the leading research methods for data collection and analysis.
9. Demonstrate a deep understanding of the ethical issues surrounding the use of data and apply ethical decision making in real-world data-related applications.
10. Demonstrate capability of analyzing, synthesizing, and evaluating knowledge from a wide range of fields and be capable of lifelong self-directed learning.

Po-Lo Matrix