Introduction to Python for Data Processing

3 EC

Semester 1, period 2

5314ITPF3Y

Owner Master Logic
Coordinator Gaelle Fontaine
Part of Master Logic, Master Logic, track Logic and Computation, Master Logic, track Logic and Philosophy, Master Logic, track Logic and Language, Master Logic, track Logic and Mathematics, Master Logic, track Logic Year, year 1Master Logic, track Exchange, year 1

Course manual 2024/2025

Course content

This course is designed to provide students with the background in programming that is necessary to follow other more advanced master-level courses in areas such as linguistics, natural language processing, machine learning, etc. The goal is to make students that have had no prior exposure to programming feel comfortable with the basic concepts.


In this course, the students will be learning Python, a language that is now widely used in many fields. Combining their Python skills with the knowledge acquired during Basic Probability: Theory,  the students will also implement several basic machine learning algorithms during the second half of the course.

Study materials

Literature

Objectives

  • the student is able to use basic programming concepts, such as variables, conditions, loops, functions, lists, etc.
  • given the mathematical description of a simple algorithm, the student is able to implement that algorithm in python
  • the student is able to use some more advanced python specific concepts such as dictionaries, classes and inheritance
  • the student is able to use a library and to find information by himself/herself about a new library using the documentation
  • the student is able to find and fix basic programming errors in a program
  • the student can independently study and search both offline and online resources
  • the student is able to implement some very basic machine learning algorithms, using the concepts from the course ‘Basic probability: theory’

Teaching methods

  • Seminar
  • Self-study

Learning activities

Activity

Hours

Tentamen

3

Werkcollege

14

Self study

67

Total

84

(3 EC x 28 uur)

Attendance

This programme does not have requirements concerning attendance (TER-B).

Assessment

Item and weight Details Remarks

Final grade

Students need at least 4.5 for the final exam in order to pass the course.

0.2 (20%)

Tentamen

0.6 (60%)

Assignments

1 (20%)

Programming assignment 2

1 (20%)

Programming assignment 4

1 (20%)

Programming assignment 3

1 (20%)

Programming assignment 1

1 (20%)

Programming assignment 5

0.033 (3%)

Self-study notebook week 1

0.033 (3%)

Self-study notebook week 2

0.033 (3%)

Self-study notebook week 3

0.033 (3%)

Self-study notebook week 4

0.034 (3%)

Self-study notebook week 5

0.034 (3%)

Self-study notebook week 6

Fraud and plagiarism

The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl

Course structure

WeeknummerOnderwerpenStudiestof
1
2
3
4
5
6
7
8

Contact information

Coordinator

  • Gaelle Fontaine