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Blekinge Institute of Technology
Department of Computer Science

Revision: 3
Reg.no: BTH-4.1.14-1494-2022


Course syllabus

Intelligent Data Analysis

Intelligent Data Analysis

6 credits (6 högskolepoäng)

Course code: DV1597
Main field of study: Computer Science, Technology
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: 2023-01-25
Approved: 2023-01-25

1. Descision

This course is established by Dean 2018-05-18. The course syllabus is approved by Head of Department of Computer Science 2023-01-25 and applies from 2023-01-25.

2. Entry requirements

For admission to the course requires completed courses Mathematical statistics, 6 credits and Programming, 12 credits.

3. Objective and content

3.1 Objective

The aim of this course is to teach students how to explore datasets containing different types of data, such as tabular, textual or time series data. The students will learn how to preprocess these datasets to address common issues, such as missing data or to reshape the data to meet the requirements of the chosen analysis technique. Furthermore, students will learn to discover and confirm relationships and patterns present in data using visualizations and statistical analysis.

3.2 Content

The course covers three main topic areas, viz. data handling, data visualization, and statistical data analysis. Within these areas, the course covers following aspects.
Data handling:
• Different types of data
• Data cleaning and missing data
• Data reformatting
Data visualization:
• Common plots for data exploration (e.g. box plots, heatmaps)
• Interactive plots
• Visualization of high-dimensional data (e.g. t-SNE)
Statistical data analysis:
• Descriptive statistics and distributions
• Regression analysis
• Parametric and non-parametric statistical tests (distribution tests, correlation, hypothesis tests)

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:

  • demonstrate the ability to explain data handling approaches suitable for different types of data.
  • demonstrate the ability to explain different data visualization techniques and their interpretation.
  • demonstrate the ability to explain different statistical data analysis techniques and their assumptions.

4.2. Competence and skills

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

  • demonstrate the ability to implement data handling pipelines for different types of data.
  • demonstrate the ability to use and adjust data visualization techniques to fit their data analysis needs.
  • demonstrate the ability to apply different statistical data analysis techniques to collect evidence for or against certain relationships in data.

4.3. Judgement and approach

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

  • demonstrate the ability to choose approriate methods for data handling and data imputation
  • demonstrate the ability to choose appropriate visualization methods for uncovering relationships and patterns in data.
  • demonstrate the ability to choose appropirate statistical analysis methods based on the given data.

5. Learning activities

The content of this course will be discussed in lectures. Students are furthermore expected to acquire additional knowledge through self study of relevant literature. Additionally to the lectures, weekly exercises will help the students to practically apply the discussed topics. 
Apart from the weekly exercises, the students will demonstrate their knowledge in one small scale project on time series analysis and one larger project in which they will analyse a provided dataset to discover present relationships and patterns in the data.

6. Assessment and grading

Modes of examinations of the course

Code Module Credit Grade
2105 Written assignment 2 credits GU
2115 Project assignment 1 credits AF
2125 Written report 3 credits AF

The course will be graded A Excellent, B Very good, C Good, D Satisfactory, E Sufficient, FX Failed result, a little more work required, F Fail.

The information before a course occasion 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
• Field Cady The Data Science Handbook Wiley, 2017, ISBN 978–1119092940

Reference Literature
• Wes McKinney Python for Data Analysis O’Reilly Media, Inc., 2012, ISBN 978– 1449319793
• Jake VanderPlas Python Data Science Handbook: Essential Tools for working with Data O’Reilly UK Ltd., 2016, ISBN 978– 1491912058

This is not a legal document. If you would like a copy of the legal decision regarding this course plan, contact the registrar at Blekinge Institute of Technology.