Course Description: This laboratory course provides hands-on experience in data manipulation techniques and strategies for handling missing data. Students will learn practical skills to effectively manage and preprocess data for analysis, with a focus on techniques such as data cleaning, transformation, and restructuring. Emphasis will be placed on utilizing popular data manipulation libraries and tools such as pandas in Python. Additionally, students will explore various methods for handling missing data, including imputation techniques and sensitivity analysis. Through a combination of guided exercises and real-world datasets, students will develop proficiency in managing and preparing data for analysis, enabling them to address common challenges encountered in data science and research projects.
Prerequisites: Basic knowledge of programming and familiarity with data analysis concepts.
