|Kursusnavn (dansk):||Introduction to Data Science and Programming |
|Kursusnavn (engelsk):||Introduction to Data Science and Programming |
|Semester:||Efterår 2017 |
|Udbydes under:||Bachelor i datavidenskab (b-ds) |
|Omfang i ECTS:||15,00 |
|Min. antal deltagere:||25 |
|Forventet antal deltagere:||0 |
|Maks. antal deltagere:||70 |
|Formelle forudsætninger:||The course is mandatory for students on first semester BSc in Data Science.
The course is only open for students enrolled in BSc in Data Science.
|Læringsmål:||After the course the students should be able to:
- identify the fundamental problems in data science
- describe the standard approaches to solving these problems
- select the appropriate methods for analysing various data types
- create computer programs for solving problems in data science
- apply basic machine learning to data analysis
- analyse heterogenous collections of data with computer programming
- visualize the results of various data analyses
|Fagligt indhold:||This course is both a practical introduction to the foundations of Data Science and to programming.
This subjects covered in the course include:
- Computer fundamentals
- Object-oriented programming with Python
- Mathematical programming
- Heterogeneous data and data curation
- Basic reporting
- Prediction using machine learning
- Network analysis
- Analysis under noisy conditions
- Big data and distributed computing
|Læringsaktiviteter:||14 ugers undervisning bestående af forelæsninger og øvelser|
There are 14 weeks of teaching activities, which are evenly split into 14 pairs of lectures and exercises.
Note that the course also includes homework assignments and group work reports, which are listed under mandatory activities and assessment (in the exam).
The lectures cover the topics in data science and computer programming as listed above in the course content. The exercise sessions involve additional hands-on experience with the topics covered in the corresponding weekly lectures. The exercises are also aimed at additionally equipping the students with the skill sets necessary for the successful completion of homework assignments and group work.
|Obligatoriske aktivititer:||The course includes 10 mandatory homework assignments in computer programming for data science. Each of these assignments requires solving a programming and/or data problem in a programming language. Each assignment is closely tied to the weekly progression of the course, and closely relates to the topics addressed in the lectures and the corresponding exercise sessions.
The homework assignments require the students to closely observe the submission deadlines. The students are required to submit correct solutions to 8 out of 10 mandatory homework assignments to be considered eligible for the exam.
The course also includes a group project which will be assessed. Groups will carry out an oral presentation of their project at two separate stages in the semester. This is mandatory.
The students will be granted a second attempt at all mandatory assignments: both the homework exercises and the group project presentations.
For each of the 10 homework exercises the second attempt deadline will be set one week after the first deadline. For the 2 group project presentations, there will be a backup presentation time slot one week after the first deadline.
Be aware: The student will receive the grade NA (not approved) at the ordinary exam, if the mandatory activities are not approved and the student will use an exam attempt.
|Eksamensform og -beskrivelse:||X: Eksperimentel eksamensform., (7-scale, internal exam)|
There will be:
- a hand-in project write-up in groups (worth 50% of the final grade)
- an individual written exam on premises (worth 50% of the final grade)
The duration of the written examination on premises is 4 hours. It is a closed-book exam on paper. No aids are allowed at the exam, other than a pen for handwriting the answers.
The reexam form will depend of the number registered for re-exam.
|Litteratur udover forskningsartikler:||1) • John Zelle. (2016). Python Programming: An Introduction to Computer Science (Third Edition). Franklin, Beedle & Associates.
2) Joel Grus. (2015). Data Science from Scratch: First Principles with Python. O’Reilly Media.