Data Pre-processing (FK_083)

The objective of the data pre-processing study course is to provide students with essential skills, to prepare raw data for analysis. The main objectives include: Understanding data pre-processing: to understand the importance of data pre-processing and the basics of data analysis workflow. Data cleaning: to learn methods how to process missing values, remove duplicates, and correct errors to ensure data accuracy and consistency. Data transformation: to transform data into suitable formats for analysis, including normalisation, scaling and categorical variable encoding. Feature engineering: to create new features from existing data to improve model performance. Invalid data processing: to identify and manage invalid data to prevent deviations during analysis. Data integration and reduction: to combine data from different sources and reduce size for effective analysis. Practical experience: to obtain practical experience with real-world datasets using industry standard tools and software. Best practices and tools: to learn best practices and familiarise with tools and libraries such as Python’s Pandas, R and SQL. Preparation for improved analysis: to ensure readiness to perform additional data analysis tasks such as machine learning and statistical analysis. Ethical considerations: to discuss ethical aspects, including data privacy and security during pre-processing. At the end of the course, students will be able to convincingly prepare raw data for different analytical applications, ensuring that they are clean, well-structured and ready to use.