The purpose of this course is to provide students with a comprehensive overview of Data Science, introducing them to key concepts, tools, and methodologies used in the field. Students will learn to identify and address common challenges encountered when working with data, and explore various techniques and approaches for solving these problems

Essential Data Science topics include data acquisition, cleaning, processing, management, analysis, and visualization. Students will be introduced to inferential statistics and basic machine learning concepts. The course also explores business intelligence and data warehousing, the iterative cycle of data science projects, and critical data privacy and security aspects.

Face-to-face lecture and lab sessions, online course materials, visual presentations, discussions, lab works

Textbook: James, G., itten, D., Hastie, T., Tibshirani, R. (2023). An Introduction to Statistical Learning with Applications in Python (in R). 2nd Ed., Springer. Other Resources: - Lecture Notes and Codes - Python for Data Analysis, Wes McKinney, O'Reilly Media, 2nd ed. (2017). - Bruce, P. Bruce, A. and Gedeck, P. (2020). Practical Statistics for Data Scientists. O’Reilly Media, ISBN: 978-1-492-07294-2. - Book: Lau, S., Gonzalez, J., Nolan, D. (2023). Learning Data Science. O’Reilly Media.