Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications (Undergraduate Topics in Computer Science) by Laura Igual (PDF)

Description

This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis. 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 techniques and tools for statistical analysis, machine learning, graph analysis, and parallel programming; 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.

User’s Reviews

Editorial Reviews: Review “This book contains a broad range of timely topics and presents interesting examples on real-life data using Python. … the book is a good addition to references on Python and data science. Students as well as practicing data scientists and engineers will benefit from the many techniques and use cases presented in the book.” (Computing Reviews, December, 2017)“The book ‘Introduction to Data Science’ is built as a starter presentation of concepts, techniques and approaches that constitute the initial contact with data science for scientists … . The style of the book recommends it to both undergraduates and postgraduates and the concluding remarks and references provide guidance for the next steps in the study of particular topics.” (Irina Ioana Mohorianu, zbMATH, Vol. 1365.62003, 2017) From the Back Cover This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis.Topics and features:Provides numerous practical case studies using real-world data throughout the bookSupports understanding through hands-on experience of solving data science problems using PythonDescribes techniques and tools for statistical analysis, machine learning, graph analysis, and parallel programmingReviews a range of applications of data science, including recommender systems and sentiment analysis of text dataProvides supplementary code resources and data at an associated websiteThis practically-focused textbook provides an ideal introduction to the field for upper-tier undergraduate and beginning graduate students from computer science, mathematics, statistics, and other technical disciplines. The work is also eminently suitable for professionals on continuous education short courses, and to researchers following self-study courses.Dr. Laura Igual is an Associate Professor at the Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Spain. Dr. Santi Seguí is an Assistant Professor at the same institution. About the Author Dr. Laura Igual is an Associate Professor at the Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Spain. Dr. Santi Seguí is an Assistant Professor at the same institution. The authors wish to mention that some chapters were co-written by Jordi Vitrià, Eloi Puertas, Petia Radeva, Oriol Pujol, Sergio Escalera, Francesc Dantí and Lluís Garrido. Read more

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐I started reading this book but had to drop it since the code is for Python 2.7.

⭐This book was written for computer scientists and I am a mathematician so I’m not quite the audience it was designed for, however it does the job I wanted it to do. I already knew some Python and wanted to understand more about the theory behind the techniques used in data science. This book gives a wide ranging review of techniques, however I would not recommend it for learning Python – it does introduce a little bit of the syntax at the beginning, but I think that in general it assumes a good knowledge of computer programming and so doesn’t spend a lot of time explaining the code. For me this was fine, especially as there are a lot of good sites online where you can become familiar with Python and it is a very straightforward language.I will say that in places the text can be a little dense and occasionally the author falls into the trap of using technical language (although some of this may be because, as I said, I’m a mathematician and not a computer scientist. Also I strongly recommend downloading the associated files to look at as in some places these are more in depth than the text in the book. There are a few typos, but they didn’t affect the meaning of the text and it’s certainly no worse than most texts I’ve read during my studies.All in all I have found it a good introductions that sweeps over a range of topics giving a basic level of comprehension. It is easy to follow and well constructed with a good flow of ideas.

⭐If you don’t know anything about datascience, this book could help you to fix that. It’s a good place to get started with datascience.

⭐

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