Python Programming for Data Science
Course content
This course is an introduction to programming in Python, with focus on data processing and analysis. It includes basic programming concepts such as data types, conditionals, loops, functions, object oriented programming, pattern matching (regular expressions), and computational complexity. In addition, it also provides technical skills relevant to the data science pipeline such as the ability to log on to an external server, and to navigate a Unix shell.
BSc Programme in Biochemistry
BSc Programme in Biology
BSc Programme in Biotechnology
BSc Programme in Molecular Biomedicine
MSc Programme in Bioinformatics
Knowledge:
After completing the course, the student will master key
programming concepts such as data-types, variables, conditionals,
loops, and functions, and have an understanding of the central
concepts in object oriented programming, pattern matching and
computational complexity. Finally, the student will have acquired a
basic understanding of a Unix/Linux environment.
Skills:
The student is capable of solving small to medium sized programming
tasks in Python, in particular tasks related to data processing and
analytics. The student can produce programs that are well-written,
well-structured, and well-commented. Finally, the student knows how
to execute scripts on a remote server, and navigate using a Unix
command line interface on such a server.
Competences:
After completing the course, the student is capable of solving the
many small to medium size programming tasks that arise in Data
Science disciplines, and is able to write well-structured and
maintainable programs in Python. The student is also capable of
running programs both locally and on remote servers, and be able to
navigate in a Unix environment.
The student
- can explain the differences between various data-types in Python and can select the relevant type for a given programming task
- can give a detailed description of conditionals and loops, and is able to explain how loops relate to the complexity of a program
- can motivate the concepts of function and module, and give examples of how these tools should be used to structure code
- can explain the basic concepts of Object Oriented Programming, and give examples of appropriate uses of classes and object
- can identify problems for which regular expressions are well suited, and is able to construct an appropriate regular expression for a given pattern matching problem
- can give examples of how to handle errors in a program
- is capable of independently finding online information about external Python modules, and applying this information to solve a specific task
Lectures and exercises mixed (6-9 hours per week). Some of the exercises will involve simple Biological examples, but a Biological background is not required for taking the course.
See Absalon.
This is an introductory programming course: no prior programming experience is required. We do assume knowledge corresponding to a basic University-level mathematics introduction course (e.g. Introduction to the Mathematics for the Chemical Sciences).
Participants are expected to bring a laptop equipped with a
network card to class (contact the teacher if not possible).
This course is equivalent to NDAB21003U Python programmering til
datavidenskab therefore it is not allowed to sign up for both
courses.
- ECTS
- 7,5 ECTS
- Type of assessment
-
On-site written exam, 4 hours under invigilation
- Type of assessment details
- The on-site written exam is an ITX exam.
See important information about ITX-exams at Study Information, menu point: Exams -> Exam types and rules -> Written on-site exams (ITX) - Exam registration requirements
-
Approval of 80% of the weekly exercises.
- Aid
- All aids allowed
The University will make computers available to students at the ITX-exam.
Students are not permitted to bring digital aids like computers, tablets, calculators, mobile phones etc.
Books, notes, and similar materials can be brought in paper form or uploaded before the exam and accessed digitally from the ITX computer. Read more about this at Study Information.
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Several internal examiners
- Re-exam
-
Same as ordinary exam.
80% of the exercises must be handed in and approved no later than three weeks before the reexamination.
If 10 or fewer students have signed up for re-exam, the type of assessment will be changed to 30 minutes oral exam, 30 minutes preparation, all aids allowed.
Criteria for exam assessment
See Learning outcome
Single subject courses (day)
- Category
- Hours
- Lectures
- 21
- Preparation
- 160
- Practical exercises
- 21
- Exam
- 4
- English
- 206
Kursusinformation
- Language
- English
- Course number
- NDAB24000U
- ECTS
- 7,5 ECTS
- Programme level
- Bachelor
- Duration
-
1 block
- Placement
- Block 1
- Schedulegroup
-
B
- Capacity
- 175
The number of places might be reduced if you register in the late-registration period (BSc and MSc) or as a credit or single subject student. - Studyboard
- Study Board for the Biological Area
Contracting department
- Department of Computer Science
- Department of Biology
Contracting faculty
- Faculty of Science
Course Coordinators
- Thomas Wim Hamelryck (8-786c65716970767d44666d73326f7932686f)
- Wouter Boomsma (2-846f4d71763b78823b7178)
Se skema
- 24E-B1-1;Hold 01;;Python Programming for Data Science
- 24E-B1-1;Hold 02;;Python Programming for Data Science
- 24E-B1-1;Hold 03;;Python Programming for Data Science
- 24E-B1-1;Hold 04;;Python Programming for Data Science
- 24E-B1-1;Hold 05;;Python Programming for Data Science
- 24E-B1-1;Hold 06;;Python Programming for Data Science
- 24E-B1-1;Hold 07;;Python Programming for Data Science
Er du BA- eller KA-studerende?
Kursusinformation for indskrevne studerende