MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 30 lectures (2h 57m) | Size: 1 GB
Learn how to read, write and visualize the raster/vector dataset and perform spatial analysis using python

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.11 GB | Duration: 5h 3m
What you'll learn

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 13 lectures (2h 4m) | Size: 601.7 MB
An overview of data center infrastructure design from a structured cabling system designer's perspective

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.18 GB | Duration: 1h 38m
R is one of the most popular and widely used tools for Data Science and Machine learning and for statistical programming.

M Is for (Data) Monkey: A Guide to the M Language in Excel Power Query By Ken Puls, Miguel Escobar
2015 | 212 Pages | ISBN: 1615470344 | EPUB | 10 MB
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Data-Driven Modeling, Filtering and Control : Methods and Applications
by Carlo Novara and Simone Formentin
English | 2019 | ISBN: 1785617125 | 304 Pages | ePUB | 6 MB

Data science with Python: The Ultimate Step-by-Step Guide for Beginners to Learn Python for Data Science by Julian James McKinnon
English | March 25, 2021 | ISBN: 1801875588 | 178 pages | MOBI | 0.48 Mb
Data analysis is just getting started. There's no limit to the amount of data available, and more companies are now interested in data analysis.

Data Analysis Using Regression and Multilevel/Hierarchical Models By Andrew Gelman, Jennifer Hill
2006 | 648 Pages | ISBN: 052168689X | PDF | 49 MB
Data Analysis Using Regression and Multilevel/Hierarchical Models is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout. Author resource page: http://www.stat.columbia.edu/~gelman/arm

Subhendu Kumar Pani, "Biomedical Data Mining for Information Retrieval: Methodologies, Techniques, and Applications "
English | ISBN: 111971124X | 2021 | 448 pages | PDF | 9 MB
This book comprehensively covers the topic of mining biomedical text, images and visual features towards information retrieval. Biomedical and Health Informatics is an emerging field of research at the intersection of information science, computer science, and health care and brings tremendous opportunities and challenges due to easily available and abundant biomedical data for further analysis. The aim of healthcare informatics is to ensure the high-quality, efficient healthcare, better treatment and quality of life by analyzing biomedical and healthcare data including patient's data, electronic health records (EHRs) and lifestyle. Previously it was a common requirement to have a domain expert to develop a model for biomedical or healthcare; however, recent advancements in representation learning algorithms allows us to automatically to develop the model. Biomedical Image Mining, a novel research area, due to its large amount of biomedical images increasingly generates and stores digitally. These images are mainly in the form of computed tomography (CT), X-ray, nuclear medicine imaging (PET, SPECT), magnetic resonance imaging (MRI) and ultrasound. Patients' biomedical images can be digitized using data mining techniques and may help in answering several important and critical questions related to health care. Image mining in medicine can help to uncover new relationships between data and reveal new useful information that can be helpful for doctors in treating their patients.

Big Data and Cloud Computing for Development: Lessons from Key Industries and Economies in the Global South By Nir Kshetri, Torbjörn Fredriksson, Diana Carolina Rojas Torres
2017 | 240 Pages | ISBN: 1138689041 | PDF | 5 MB
This book provides a framework for evaluating big data and cloud computing based on how they evolve to fit users' needs in developing countries in key areas, such as agriculture and education. The authors discuss how this framework can be utilized by businesses, governments, and consumers to accelerate economic growth and overcome information and communication barriers. By examining the ways in which cloud computing can drive social, economic, and environmental transformation, readers gain a nuanced understanding of the opportunities and challenges these technologies offer. The authors also provide an authoritative and up-to-date account of big data's diffusion into a wide range of developing economies, such as Brazil and China, illustrating key concepts through in-depth case studies. Special attention is paid to economic development in the context of the new Sustainable Development Goals formulated by the United Nations, introducing readers to the most modern standard of economic evaluation. Students of information management, entrepreneurship, and development, as well as policy makers, researchers, and practitioners, will find Big Data and Cloud Computing for Development an interesting read and a useful reference source.