Название: Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications 2nd EditionАвтор: Laura Igual, Santi Seguí
Издательство: Springer
Серия: Undergraduate Topics in Computer Science
Год: 2024
Страниц: 255
Язык: английский
Формат: pdf
Размер: 10.1 MB
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.
Data Science is a complex, multifaceted field that can be approached from several points of view: ethics, methodology, business models, how to deal with Big Data, data engineering, data governance, etc. Each point of view deserves a long and interesting discussion, but the approach adopted in this book focuses on analytical techniques, because such techniques constitute the core toolbox of every data scientist and because they are the key ingredient in predicting future events, discovering useful patterns, and probing the world. Data Science is about evidence-based storytelling and this kind of process requires appropriate tools. The Python data science toolbox is one, not the only, of the most developed environments for doing data science. You can easily install all you need by using Anaconda1 : a free product that includes a programming language (Python), an interactive environment to develop and present data science projects (Jupyter notebooks), and most of the toolboxes necessary to perform data 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).
Topics and features:
Provides numerous practical case studies using real-world data throughout the book
Supports understanding through hands-on experience of solving Data Science problems using Python
Describes concepts, techniques and tools for statistical analysis, Machine Learning, graph analysis, natural language processing, deep learning and responsible Data Science
Reviews a range of applications of Data Science, including recommender systems and sentiment analysis of text data
Provides supplementary code resources and data at an associated website
Target Audiences:
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. However, even if the reader is new to Python, this should not be a problem, since acquiring the Python basics is manageable in a short period of time.
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