Requirements: .PDF reader, 10 MB
Overview: This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the interdisciplinary field of Data Science. The coverage spans key concepts from statistics, Machine Learning/Deep Learning and responsible Data Science, useful techniques for network analysis and natural language processing (NLP), and practical applications of Data Science such as recommender systems or sentiment analysis. This book includes three different kinds of chapters. The first kind is about Python extensions. Python was originally designed to have a minimum number of data objects (int, float, string, etc.); but when dealing with data, it is necessary to extend the native set to more complex objects such as (NumPy) numerical arrays or (Pandas) data frames. The second kind of chapter includes techniques and modules to perform statistical analysis and Machine Learning. Finally, there are some chapters that describe several applications of Data Science, such as building recommenders or sentiment analysis. The composition of these chapters was chosen to offer a panoramic view of the Data Science field, but we encourage the reader to delve deeper into these topics and to explore those topics that have not been covered: big data analytics and more advanced mathematical and statistical methods (e.g., Bayesian statistics). This book is addressed to upper-tier undergraduate and beginning graduate students from technical disciplines. Moreover, this book is also addressed to professional audiences following continuous education short courses and to researchers from diverse areas following self-study courses. Basic skills in computer science, mathematics, and statistics are required. Code programming in Python is of benefit.
Genre: Non-Fiction > Tech & Devices
Download Instructions:
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https://rapidgator.net/file/759e5aa7672 … d.rar.html.
