Principles and Techniques for Data Science
- 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
Level:
Basic level
Application code:
X3002
Entry requirements:
The courses Introduction to Data Science 15 credits and Linear Algebra for Data Science 7.5 credits. English 6. Exemption of the requirement in Swedish is granted.
Selection rules:
Available for exchange students. Limited numbers of seats.
Start week:
week: 36
Instructional time:
Daytime
Language of instruction:
Teaching is in English.
