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Presentation
Presentation
Developing skills in the world of programming through basic knowledge in creating an algorithm and analysing data, thereby teaching the ability to solve problems. The programming language is just a vehicle to express algorithmic solutions. Python was chosen because it has advantages such as: high popularity; easy syntax; a significantly high number of libraries.
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Class from course
Class from course
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Degree | Semesters | ECTS
Degree | Semesters | ECTS
Bachelor | Semestral | 3
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Year | Nature | Language
Year | Nature | Language
1 | Mandatory | Português
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Code
Code
ULHT41-2310
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Prerequisites and corequisites
Prerequisites and corequisites
Not applicable
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Professional Internship
Professional Internship
Não
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Syllabus
Syllabus
Introduction to computing:
- History of computing.
- Components of a computer system.
- Operating systems.
- High and low level programming languages.
Algorithms:
- Algorithms and representations (flowcharts and pseudocode).
- Data types and variables
- Arithmetic and Logical Expressions
- Sequential, Repetition and Selection
- Structures Implementing Algorithms
Programming in Python:
- Data types and variable types in Python.
- Mathematical and logical expressions in Python.
- Sequential structures in Python, including loops and conditional selection.
- Implementing algorithms in Python.
- Python programme development.
- Lists, tuples and dictionaries in Python.
- Dataframe manipulation.
- String manipulation in Python.
- Graph creation (using libraries such as Matplotlib).
Database (MySQL).
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Objectives
Objectives
Understand the objectives of the programme and its application to industrial management. Know the different variables and develop skills in dealing with them. Master the application of essential functions and flow control structures (condition and repetition). Master the creation of programmes following an organised approach. Become familiar with current programming and data analysis tools and algorithms to support industrial management.
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Teaching methodologies and assessment
Teaching methodologies and assessment
Methodology
Active and deductive methods, through exposition with the support of slide projection and interaction between students in solving problems, awakening a critical spirit and a spirit of collaboration.
Assessment
Continuous assessment:
Attendance, punctuality: weight of 10%.
Practical work: weight of 30%.
Frequencies on the various subjects: weight of 60%.
The student will pass the continuous assessment if the weighted average is equal to or greater than 10 values.
Appeal exam:
The student will pass if the result of the exam is equal to or greater than 10 values.
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References
References
- Mark Lutz(2013). Learning python. 5th edition. O'Reilly Media. ISBN 9781449355739.
- B. Miller and D. Ranum(2009). Python: programming in context. 3th edition. Jones and Bartlett. ISBN 9781284176520
- David J. Pine(2019) Introduction to Python for Science and Engineering. CRC Press. ISBN-10: 1138583898
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Office Hours
Office Hours
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Mobility
Mobility
No
