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The course gives students who do not have experience with programming, a first introduction and basic skills in (mainly imperative) programming and scripting, using Python 3.
The student can solve simple programming problems independently, and structure these in the language Python. Most of the learned principles can be applied to other computer languages used in data science (e.g. R) as well.
Topics include:
- Expressions, assignment, statements
- Basic datatypes
- Control structures (e.g. conditional execution, loops)
- Using and defining functions
- Lists / composite datatypes (e.g. dictionaries, tuples)
- Files, text I/O
- Exceptions, assertions
- Debugging
- Basic knowledge of objects and methods
- Modules
- Algorithms
After passing this course, the student is able to
- Understand the programming terminology, concepts, code and structure. and is able to evaluate code and reason about it.
- Write maintainable, readable (Python) code that solves (simple) programming problems, in a structured way; chooses proper techniques. As part of this, interprets requirements and uses a programming environment effectively and efficiently
Literature
We are planning on using "How to think like a computer scientist: Learning with Python 3" (subject to change), currently available online free of charge. A blend of other articles, class notes and/or material may be used.
Student who want an additional 'paper' book to read up on several Python programming topics, may consider Python Programming: An introduction to computer science by John Zelle (2nd edition; a 3rd edition is planned later in 2016). The course is not dependent on that book though.
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Type of instructions
(2 hour) Lectures and (2/3 hours) labs
Type of exams
Written exam and practical assignments
Compulsory Reading
- A blend of research articles, class notes, and material from reference books will be used in this course..
Specifics
This course is part of the premaster’s program Data Science and Entrepreneurship, consisting of:
- JBP021-B-6 Programming
- JBP031-B-6 Data-structures and Algorithms
- JBP041-B-6 Introduction to Machine Learning
- JBP051-B-6 Foundations of Databases
- JBP061-B-6 Statistics for Data Scientists
The courses from the Data Science and Entrepreneurship program require specific prior knowledge. It is only possible to participate in this course if approved by the admission committee and if you are enrolled for the program. Please note that this course will be taught in Mariënburg, ‘s-Hertogenbosch (JADS).
Minor students
Are you enrolled for a BSc program and you want to follow the courses of the premaster’s courses during your BSc program, this might be possible as a minor student (in Dutch: bijvakstudent). A hard requirement is that your examination program (as presented in your study programs Education and Examination Regulations (in Dutch: OER)) contains at least 10 ECTS in Mathematics and 10 ECTS in Statistics. Do you want to know if you are eligible? Contact the admissions officer (admissions-m-datascience@tilburguniversity.edu).
Incoming exchange students
For incoming exchange students: you should have passed at least 10 ECTS in Mathematics and 10 ECTS in Statistics in order to be eligible for this course.
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