Students will acquire a comprehensive understanding of data science process, including fundamental concepts and data analysis techniques. They will realize the functioning of these techniques and develop an intuitive for building intelligent applications based on a data-driven approach. Furthermore, they will gain insight into the types of questions that can be addressed through data analysis and will develop the ability to apply and evaluate machine learning methods. Special emphasis is placed on data analysis and data preprocessing, with a coverage of basic machine learning methods, their evaluation, and comparison.
- NGUYEN, G., 2022. Introduction to Data Science. The Edition of University Textbooks on Informatics and Information Technologies. Spektrum STU Publishing, ISBN 978-80-227-5193-3.
Available at FIIT STU e-library ELVIRA with AIS access.
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GÉRON, A., 2022. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O'Reilly Media, Inc. ISBN 978-1098125974. 3rd edition.
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MCKINNEY, W., 2022. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and Jupyter. O'Reilly Media, Inc. ISBN 978-1098104030. 3rd edition.
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SHAW, Z.A., 2017. Learn Python 3 The Hard Way: A very simple introduction to the terrifyingly beautiful world of computers and code. Addison-Wesley Professional. ISBN 978-0134692883.
