Principles and Techniques for Data Science

Utbildningsfakta

Kod: 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

Behörighet

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.

Utbildningstillfällen

Varje tillfälle har egna datum och villkor. Avslutade tillfällen behålls som historik och innebär inte att en ny ansökan är öppen.

• Principles and Techniques for Data Science

  Halmstad University

  Startdatum: 31 augusti 2026

  Slutdatum: 1 november 2026

  Studietakt: 50 %