Data Science: Foundations and Hands-on Experience

Dr. Fatwa Ramdani is currently a Professor at the University of Tsukuba, Japan, within the Graduate School of Humanities and Social Sciences. A graduate of Tohoku University, Japan, his research expertise lies in Geoinformatics, combining geography with information science and technology to analyze spatial phenomena and geospatial datasets. His work provides unique insights into changes in the human-physical environment, addressing fundamental challenges in physical, environmental, and social sciences.  Before joining the University of Tsukuba, Dr. Ramdani was a lecturer in the Information Systems Department at Brawijaya University, Indonesia, where he also served as the Director of the Geoinformatics Research Group. Dr. Ramdani has authored several books, including "Exploring the Earth with QGIS: A Guide to Using Satellite Imagery at Its Full Potential," published by SpringerNature. He has also published numerous peer-reviewed articles in conferences and journals, contributing significantly to the field of environmental impact assessment and geospatial data analysis.  He is affiliated with esteemed organizations such as the IEEE Geoscience and Remote Sensing Society (IGARSS), the Japan Society of Photogrammetry and Remote Sensing (JSPRS), the Remote Sensing Society of Japan (RSSJ), Japan Geographic Information System Association (GISA) and the European Geosciences Union (EGU). Dr. Ramdani actively serves as an editor and reviewer for various journals, including "Nature", "Climatic Change", "Remote Sensing," "Applied Sciences," "AgriEngineering", and "Network: Computation in Neural Systems."  In addition to his research, Dr. Ramdani teaches courses such as "Data Science for Social Sciences" and "Introduction to Geographic Information Systems" at the University of Tsukuba. His dedication to advancing geospatial science and technology continues to contribute to understanding and addressing environmental and societal challenges.

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• Introduction to Data Science & Process of Data Science.- Data Types & Measurement Scale.- Data Exploration, Preprocessing, & Modeling.- Statistics - Descriptive & Inferential.- Data Visualization & Uncertainty.- Machine Learning, Measuring Uncertainty, and Forecasting.- Working with Spatial Data.- Web Scraping & Data Mining.- Natural Language Processing & Sentiment Analysis.- Ethics & Reproducibility.