Official course description, subject to change:

Basic info last published 18/04-24
Course info
Language:
English
ECTS points:
15
Course code:
BSINDSP1KU
Participants max:
95
Offered to guest students:
no
Offered to exchange students:
no
Offered as a single subject:
no
Programme
Level:
Bachelor
Programme:
BSc in Data Science
Staff
Course manager
Vedran Sekara
Associate Professor
Teacher
Martin Aumüller
Associate Professor
Course semester
Semester
Efterår 2024
Start
26 August 2024
End
24 January 2025
Exam
Exam type
ordinær
Internal/External
intern censur
Grade Scale
7-trinsskala
Exam Language
GB
Abstract
This course is an introduction to programming, data science and related foundations.
Description
The subjects covered in the course include: 
  • Computer fundamentals
  • Problem analysis, program design and implementation
  • Python programming: 
    • Simple data types, methods, fields, variables, expressions
    • Objects, special Python objects (Strings, Lists, Files), classes and class design, object-oriented design (object-oriented concepts: encapsulation, polymorphism, inheritance)
    • Basic logic and its relation to Boolean types and operations, decision structures
    • Loop structures
    • Data collections
    • Algorithm design and recursion
    • Testing and documentation
  • Foundational maths for programming and Data Science:
    • Basic formal reasoning (basic first order logic, set theory, sequences and sums)
    • Basic graph theory
  • Introductory Data Science:
    • Basic statistical and visual summaries and basic reporting
    • Data curation and preparation for analysis
    • Basic Machine Learning
    • Basic definitions of graph theory and basic Network Analysis
    • Big data and distributed computing 
Formal prerequisites
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.
Intended learning outcomes

After the course, the student should be able to:

  • Test the correctness of a piece of code or small program (including explaining whether it works as desired, and measuring to what degree the testing supports such conclusions)
  • Design and implement a program.
  • Analyze a given computational task and construct a short program in Python to solve it.
  • Create and explain basic statistical and visual summaries for datasets.
  • Create and explain basic data science analyses (basic machine learning and basic network analysis).
  • Reason about the computational complexity of an algorithm or process.
  • Reflect on how to prepare data for data science analysis.
Ordinary exam
Exam type:
A: Written exam on premises, Internal (7-point scale)
Exam variation:
A22: Written exam on premises with restrictions.