Official course description, subject to change:
Basic info last published 26/03-21

Introduction to Data Science and Programming

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
Associate Professor
Teacher
Postdoc
Course semester
Semester
EfterÄr 2021
Start
30 August 2021
End
31 December 2021
Exam
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 probabilities
    • Induction and recursion
    • 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:

  • Analyse a problem, with an aim to construct a short program in Python script to solve it.
  • Design a program (given an analysis).
  • Implement a program (given a design).
  • Test the program (including explaining whether it works as desired, and measuring to what degree the testing supports such conclusions).
  • Apply basic Python constructions.
  • Evaluate and explain whether or how a basic Python construction is appropriate to solve a problem.
  • Describe what a data science project is.
  • Prepare data for a data science analysis.
  • Create and explain basic statistical and visual summaries for datasets.
  • Assess basic probabilities for events.
  • Create and explain basic data science analyses (basic machine learning and basic network analysis).
  • Describe and apply formal definitions, and construct induction proofs.
  • reflect on and apply basic data structures for data science
Ordinary exam
Exam type:
A: Written exam on premises, Internal (7-point scale)
Exam variation:
A33: Written exam on premises on paper with restrictions