*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 semester

Semester

EfterĂ„r 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 premisesÂ on paper with restrictions