Principles and Techniques for Data Science

Education facts

Code: DS4007

- Introduction: different components of the data science life cycle - Classification of different methods for data processing: retrieval, structuring - Cleaning, transformation, etc., and description of various data types - Introduction of different paradigms for exploratory data analysis, such as statistics - Analysis, scores, ranking, hypothesis testing, and data visualization - Overview of data mining techniques for understanding trends, outliers, and patterns from large amounts of data - Presentation of various methods for predictive modeling - Introduction of data science tools: programming, computing environments, and big data infrastructures - Presentation of data ethics: privacy, security, fairness, bias, and interoperability - Providing guidance on how to do data science project

Entry requirements

The courses Introduction to Data Science 15 credits and Linear Algebra for Data Science 7.5 credits. English 6 or English level 2. Exemption of the requirement in Swedish is granted for those with foreign grades.

Education offerings

Each offering has its own dates and conditions. Closed offerings are retained as history and do not mean that a new application is open.

• Principles and Techniques for Data Science

  Halmstad University

  Start date: 31 August 2026

  End date: 1 November 2026

  Pace of study: 50 %