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Programming Fundamentals

Code: EIC0005     Acronym: FPRO

Keywords
Classification Keyword
OFICIAL Programming Fundamentals

Instance: 2019/2020 - 1S Ícone do Moodle

Active? Yes
Web Page: https://web.fe.up.pt/~jlopes/doku.php/teach/fpro/index
Responsible unit: Department of Informatics Engineering
Course/CS Responsible: Master in Informatics and Computing Engineering

Cycles of Study/Courses

Acronym No. of Students Study Plan Curricular Years Credits UCN Credits ECTS Contact hours Total Time
MIEIC 193 Syllabus since 2009/2010 1 - 6 70 162

Teaching Staff - Responsibilities

Teacher Responsibility
João António Correia Lopes

Teaching - Hours

Lectures: 3,00
Recitations: 2,00
Type Teacher Classes Hour
Lectures Totals 1 3,00
João António Correia Lopes 3,00
Recitations Totals 8 16,00
Ricardo Pereira de Magalhães Cruz 4,00
Fernando José Cassola Marques 4,00
Rui Carlos Camacho de Sousa Ferreira da Silva 4,00
João António Correia Lopes 4,00
Mais informaçõesLast updated on 2019-09-20.

Fields changed: Teaching methods and learning activities, Observações, Programa

Teaching language

Portuguese

Objectives

1 - BACKGROUND

Fluency in the process of software development is a basic prerequisite to the work of Informatics Engineers. In order to use computers to solve problems effectively, students must be competent at reading and writing programs using higher-order programming languages.

2 - SPECIFIC AIMS

The global aim of this Unit is to give the student the ability to create algorithms, and to use a programming language to implement, test, and debug algorithms for solving simple problems.

The student will be able to understand and use the fundamental programming constructs, and the functional approach to programming, specifically effect-free programming where function calls have no side-effects and variables are immutable, and contrast it with the Imperative approach.

3 - PERCENTUAL DISTRIBUTION

Scientific component: 40%
Technological component: 60%

Learning outcomes and competences

At the end of the course, the student is expected to handle programming problems of medium complexity, using the imperative or functional programming approaches or paradigms.

More specifically, the student will be able to:

  1. Design, implement, test and debug a program that uses the fundamental programming constructs, basic computations, standard conditional and iterative structures, simple Input/Output, persistence and exceptions.
  2. Understand Data Abstraction and use simple and aggregate data types.
  3. Understand Procedural Abstraction and use the definition of functions, parameter passing, recursion.
  4. Write useful functions that take and return other functions.
  5. Implement basic algorithms that avoid assigning to mutable state or considering reference equality.
  6. Correctly reason about variables and lexical scope in a program using function closures.
  7. Define and use iterators, generators and other operations on aggregates, including operations that take functions as arguments (especially map, reduce/fold and filter).
  8. Use the programming tools that help writing, testing and documenting computer programs according to the programming best practices.

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

No previous knowledge is required.

Program


  • Introduction to Computational Thinking, algorithms and programming with Python.

  • Fundamental programming concepts: simple data types; variables, expressions and statements; program flow, conditionals, iteration; functions, parameter passing, recursion; persistence.

  • Working with aggregate data types: strings, tuples, sets, lists, dictionaries.

  • Effect-free programming by using function calls without side effects and immutable variables.

  • First-class functions and closures.

  • Utility higher-order functions: map, reduce/fold and filter.

  • Generators and list comprehensions.

  • Problem-solving strategies.

  • Programming tools, debugging, exceptions.

Mandatory literature

Peter Wentworth, Jeffrey Elkner, Allen B. Downey, and Chris Meyers; How to Think Like a Computer Scientist — Learning with Python 3 (Freely avaliable online in PDF and HTML)

Complementary Bibliography

Steven F. Lott; Building Skills in Python - A Programmer's Introduction to Python, FreeTechBooks, 2010 ([HTML])
Allen Downey; Think Python - How to Think Like a Computer Scientist, Green Tea Press, 2nd Edition, Version 2.2.23, 2015 ([HTML] [PDF])
David Mertz; Functional Programming in Python, O’Reilly Media, 2015 ([PDF])
Ernesto Costa; Programação em Python. ISBN: 978-972-722-816-4

Teaching methods and learning activities

The continuous enrolment of the student in the course is promoted, through the study and discussion of the course topics, previously distributed in Jupyter Notebooks, both in lectures and recitation classes and with in-class and away programming assignments.

The student is motivated to find the best ideas to solve specific problems, execute them and implement the programming solutions, in an elegant, legible and efficient (time and space) mode using the Python programming language.

Automatic correction tools are used to increase the efficiency of the feedback given to the students.

Lecture classes (T) are used to present and discuss the topics of the program, using a computer connected to a multimedia projector.

Recitation classes (TP) are used to help students understand the topics of the program and solve the weekly programming assignments.

In-class and away programming assignments are given, on a weekly basis, to improve the regular and effective development of autonomous learning processes and are tested and graded using Moodle assignments.

In-class assignments are used for summative evaluation at the end of each lecture class using Moodle quizzes.

Students are encouraged to use a Web App (Play) containing programming exercises selected by theme: exercises (easy) to perform before the theoretical class, exercises (of average difficulty) to perform before the weekly assignments (RE) and exercises (difficult) to perform before the practical tests in computer (PE).

Students are exposed, in the classroom work, to the use of the same work environment (IDE Spyder, PEP8 and pylint and submission of answers) that is used later in the individual evaluations (PE).

Students are encouraged to go to the "Clinic", which is supervised by monitors, in their first weeks of classes and whenever necessary in the classroom, especially during the first month.

Software

Anaconda Distribution 2019.03 for Linux (https://www.anaconda.com)
Spyder3 (included in Anaconda)
Python 3.7 (included in Anaconda)

keywords

Physical sciences > Computer science > Programming

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Designation Weight (%)
Teste 80,00
Trabalho prático ou de projeto 20,00
Total: 100,00

Amount of time allocated to each course unit

Designation Time (hours)
Estudo autónomo 92,00
Frequência das aulas 70,00
Total: 162,00

Eligibility for exams

Students are eligible for the final theory exam (TE), if they do not exceed the absences limit (25% of the total number of estimated recitation classes) and if they obtain a minimum of 40% at least in one of the two last practical on computer evaluations (MAX(PE04, PE05) >= 40%).

Calculation formula of final grade

The evaluation will be based on the following components:

LE = Lecture in-class evaluation: Multiple-choice questions about programming concepts, to be answered on an individual basis in the theoretical classroom (the best 20 from a total of 26 are selected)
RE = Recitation and away weekly evaluation: Weekly recitation classes and away programming assignments (the best 10 out of 13 are selected)
PE = Practical on computer evaluation: Individual programming assignments in Moodle (the best 3 from a total of 5 are selected)
TE = Theory evaluation: Multiple-choice questions, about programming concepts, to be answered on an individual basis in Moodle with the consultation of one book

Final classification = 10% LE + 10% RE + 50% PE + 30% TE

Observations:

  1. A minimum classification of 40% in the component TE is required.
  2. A minimum mark of 40% is required for at least one of the last two practical on computer evaluation (PE04 or PE05).
  3. There will be another TE (TE02) for students, eligible for the exam, that miss the examination with proper justification, or students that did not obtain the minimum required in the component. In the later case, the grade is limited to 50%.
  4. If the final classification is greater than 17, the student may be submitted to an oral examination and the final grade is the average of both grades.
  5. Students which achieve at least 90% in the in-class and weekly programming assignments (LE + RE), by the middle of the course, may opt to retain the grade and do a small project to achieve the maximum grade (100%).

Examinations or Special Assignments

The evaluation of this course uses three types of examinations:

  1. In-class and away programming assignments related with the current topics, to be submitted in Moodle and automatically graded.
  2. Practical on computer evaluation of a set of small computer programs, using Moodle and automatically graded, with consultation of given languages reference texts.
  3. Theory evaluation with one book consultation using a multiple-choice questionnaire in Moodle to assess students ability to use and discuss the best algorithms and data structures for specific problems.

Special assessment (TE, DA, ...)

In the case of students who do not attend classes and go without the weekly evaluation (LE and RE), the final classification will be the average of the classification of two evaluation components: Practical on computer evaluation (PE) and Theory evaluation (TE), as of the regular students.

Final classification = 60% PE + 40% TE

Observations:

  1. A minimum classification of 40% of each component, PE and TE, is required.
  2. A minimum mark of 40% is required for at least one of the last two practical on computer evaluation (PE04 or PE05)
  3. There will be another TE (TE02) for students, eligible for the exam, that miss the examination with proper justification, or students that did not obtain the minimum required in the component. In the later case, the grade is limited to 50%.
  4. If the final classification is greater than 17, the student may be submitted to an oral examination and the final grade is the average of both grades.

Classification improvement

In the following academic year, the student can request improvement of classification that includes the practical evaluation in computer PE04 or PE05 and the theoretical evaluation TE01 or TE02, with consultation of only one book, the same as for the regular students.

Final classification = 60% PE + 40% TE

Observations:

  1. A minimum classification of 40% of each component, PE and TE, is required.
  2. If the final classification is greater than 17, the student may be submitted to an oral examination and the final grade is the average of both grades.

Observations

No individual component grades (LE, RE, PE, TE) can be reused in another enrolment in the course.
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