Print as pdf if you want a pdf! If you want a nicer printout, click off the browser's automatically added header and footer.

Blekinge Institute of Technology
Department of Computer Science

Revision: 2
Reg.no:


Course syllabus

Interactive Laboratory Exercises

Interactive Laboratory Exercises

2 credits (2 högskolepoäng)

Course code: DV1604
Main field of study: Computer Science
Disciplinary domain: Technology
Education level: First-cycle
Specialization: G1F - First cycle, has less than 60 credits in first-cycle course/s as entry requirements

Language of instruction: English
Applies from: 2020-01-20
Approved: 2020-06-22

1. Descision

This course is established by Dean 2018-09-19. The course syllabus is approved by Head of Department of Computer Science and Engineering 2020-06-22 and applies from 2020-01-20.

2. Entry requirements

For admission to the course, courses in programming, 6 credits, are required.

3. Objective and content

3.1 Objective

The aim of the course is to introduce an interactive environment for program development, experimentation, data analysis, visualization and documentation. Examples of the type of environment that can be used in the course are IPython and Jupyter Notebook. The course will also give an introduction to several of the software libraries available for data analysis in Python. The goal is for the student to become familiar with an interactive environment for data analysis that will be used during much of the education.

3.2 Content

The course reviews the basic functionalities of interactive development environments for Python, making a special emphasis in the analysis and visualization of data. The course briefly reviews basic statistical concepts and introduces some of the common Python libraries used in data science and scientific visualization.

The course explores:

  • Fundamentals of statistics (distributions, deviations, regressions, hypothesis testing, etc.)
  • Interactive Python development with Jupyter Notebook.
  • Fundamentals of data visualization (readability, data representation techniques).
  • Python for data processing and statistical analysis, with Pandas, NumPy and/or SciPy.
  • Python for statistical and scientific visualization, with Matplotlib, Seaborn and/or VisPy.

4. Learning outcomes

The following learning outcomes are examined in the course:

4.1. Knowledge and understanding

On completion of the course, the student will be able to:

  • Understand the basics of statistical data analysis.
  • Understand the principles of data visualization and the criteria for their appropriate use.

4.2. Competence and skills

On completion of the course, the student will be able to:

  • Use Jupiter Notebook to develop, compile and document data analyzes and visualizations.
  • Implement mathematical and statistical techniques using Pandas, NumPy or SciPy.
  • Implement different plotting techniques to visualize data using Matplotlib, Seaborn or VisPy.
  • Use creativity and logical skills to implement appropriate visualization techniques.
  • Apply interactive graphical interfaces, widgets and commands in Jupyter Notebooks, to analyze and manipulate data.
  • Export and share interactive Notebooks.
  • Apply theoretical principles in practice.

4.3. Judgement and approach

On completion of the course, the student will be able to:

  • Motivate the selection of an appropriate statistical methods based on the type of analysis and data.
  • Interpret results and deliver conclusions based of statistical and graphical evidence.

5. Learning activities

The course is given as a classroom course. Learning activities include lectures, labs, and a final course project. The course makes use of the BTH learning platform to distribute class materials and manage learning activities.

6. Assessment and grading

Modes of examinations of the course

Code Module Credit Grade
2010 Project Assignment 2 credits GU

The course will be graded G Pass, Ux Failed result, a little more work required, U Fail.

The project assignment can be examined in writing and orally.

The information before the start of the course states the assessment criteria and make explicit in which modes of examination that the learning outcomes are assessed.

An examiner can, after consulting the Disability Advisor at BTH, decide on a customized examination form for a student with a long-term disability to be provided with an examination equivalent to one given to a student who is not disabled.

7. Course evaluation

The course evaluation should be carried out in line with BTH:s course evaluation template and process.

8. Restrictions regarding degree

The course can form part of a degree but not together with another course the content of which completely or partly corresponds with the contents of this course.

9. Course literature and other materials of instruction

Main literature
Title: Python Data Science Handbook
Author: Jake VanderPals.
Publisher: O’Reilly Media, Inc.
Published: 2016
ISBN: 9781491912058
URL: https://learning.oreilly.com/library/view/python-data-science/9781491912126/


Other relevant literature
Title: Jupyter for Data Science
Author: Dan Toomey.
Publisher: Packt Publishing
Published: 2017
ISBN: 9781785880070
URL: [https://learning.oreilly.com/library/view/jupyter-for-data/9781785880070/]()

Title: IPython Interactive Computing and Visualization Cookbook - Second Edition
Author: Cyrille Rossant
Publisher: Packt Publishing
Published: 2018
ISBN: 9781785888632
URL: https://learning.oreilly.com/library/view/ipython-interactive-computing/9781785888632/

Title: Jupyter Cookbook
Author: Dan Toomey
Publisher: Packt Publishing
Published: 2018
ISBN: 9781788839440
URL: https://learning.oreilly.com/library/view/jupyter-cookbook/9781788839440/