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Data Science Programming 7,5 Credits

Course Contents

The course is a basic course in data science programming with Python and R. The course covers basic language features and concepts, including core libraries for data science programming, such as data management and augmentation, data analysis and visualization, machine learning and model evaluation, alternating theory with practice. After completing the course, the student shall have acquired a basic, but broad, knowledge in the field of data science programming. Specifically, the student should understand and be able to apply all theoretical concepts covered.

The course includes the following elements:
- Syntax and Semantics: basic language features for the programming languages Python and R
- Data Management: importing, exporting, transforming, representing and manipulating data
- Data Augmentation: missing value imputation, discretisation and dimensionality reduction
- Data Analysis and Visualization: libraries for statistical data analysis and visualization
- Machine Learning: libraries for supervised and unsupervised machine learning
- Evaluation and Performance: evaluation metrics and significance testing
- Overview of other (besides Python and R) Data Science Programming environments

Prerequisites

Passed courses at least 90 credits within the major subject Computer Engineering, Electrical Engineering (with relevant courses in Computer Engineering), or equivalent, or passed courses at least 150 credits from the programme Computer Science and Engineering, and completed courses Data Science, 7,5 credits and Machine Learning, 7,5 credits, or equivalent. Proof of English proficiency is required.

Level of Education: Master
Course code/Ladok code: TDPS22
The course is conducted at: School of EngineeringLast modified 2021-06-07 10:51:57