An introduction to the basics of programming using the Python programming language, with an emphasis on programming of relevance to scientists.
COMP 151 introduces programming in a way that is targeted towards the needs of scientists and those working with data. Although the paper is called "Programming for Scientists", it is equally useful for anyone interested in programming in general, or in programming with data specifically. COMP 151 uses the Python programming language which is the most popular language for data science applications, and the 3rd most popular language overall and continues to grow in popularity.
| Paper title | Programming for Scientists |
|---|---|
| Paper code | COMP151 |
| Subject | Computer and Information Science |
| EFTS | 0.1500 |
| Points | 18 points |
| Teaching period | Semester 1 (On campus) |
| Domestic Tuition Fees (NZD) | $1,141.35 |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Restriction
- COMP 150
- Schedule C
- Arts and Music, Commerce, Science
- Contact
Professor Brendan McCane: brendan.mccane@otago.ac.nz
- Teaching staff
Professor Brendan McCane: brendan.mccane@otago.ac.nz
Karen Gray: karen.gray@otago.ac.nz- Paper Structure
Two Lectures and two labs per week.
Programming is a skill that requires incremental mastery of concepts. Therefore, COMP 151 uses the mastery model of learning and is 100% internally assessed.- Textbooks
The course workbook is available free online for enrolled students or can be printed at cost.
- Graduate Attributes Emphasised
- Interdisciplinary perspective, Lifelong learning, Communication, Critical thinking,
Cultural understanding, Ethics, Information literacy.
View more information about Otago's graduate attributes. - Learning Outcomes
By completion of this paper students are expected to:
- Understand fundamental concepts relating to computer programming
- Demonstrate the ability to write small computer programs
- Develop an understanding of the basic needs of scientific programming including but not limited to: data input and output, data manipulation, and data visualisation
- Develop a basic understanding of ethical and best practice issues associated with collecting and storing data, including indigenous data
