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Introduction to Data Science; A Python Approach to Concepts, Techniques and Applications

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this era, where a huge amount of information from different fields is
gathered and stored, its analysis and the extraction of value have become
one of the most attractive tasks for companies and society in general. The
design of solutions for the new questions emerged from data has required
multidisciplinary teams. Computer scientists, statisticians, mathematicians,
biologists, journalists and sociologists, as well as many others are now
working together in order to provide knowledge from data. This new
interdisciplinary field is called data science.
The pipeline of any data science goes through asking the right
questions, gathering data, cleaning data, generating hypothesis, making
inferences, visualizing data, assessing solutions, etc.
Organization and Feature of the Book
This book is an introduction to concepts, techniques, and applications in
data science. This book focuses on the analysis of data, covering concepts
from statistics to machine learning, techniques for graph analysis and
parallel programming, and applications such as recommender systems or
sentiment analysis.
All chapters introduce new concepts that are illustrated by practical
cases using real data. Public databases such as Eurostat, different social
networks, and MovieLens are used. Specific questions about the data are
posed in each chapter. The solutions to these questions are implemented
using Python programming language and presented in code boxes properly
commented. This allows the reader to learn data science by solving
problems which can generalize to other problems.
This book is not intended to cover the whole set of data science methods
neither to provide a complete collection of references. Currently, data
science is an increasing and emerging field, so readers are encouraged to
look for specific methods and references using keywords in the net.
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.
Previous Uses of the Materials
Parts of the presented materials have been used in the postgraduate course
of Data Science and Big Data from Universitat de Barcelona. All
contributing authors are involved in this course.
Suggested Uses of the Book
This book can be used in any introductory data science course. The
problem-based approach adopted to introduce new concepts can be useful
for the beginners. The implemented code solutions for different problems
are a good set of exercises for the students. Moreover, these codes can serve
as a baseline when students face bigger projects.
Supplemental Resources
This book is accompanied by a set of IPython Notebooks containing all the
codes necessary to solve the practical cases of the book. The Notebooks can
be found on the following GitHub repository: https:// github. com/
DataScienceUB/ introduction-datascience-python-book .
Acknowledgements
We acknowledge all the contributing authors: J. Vitrià, E. Puertas, P.
Radeva, O. Pujol, S. Escalera, L. Garrido, and F. Dantí.

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Ketersediaan — Sekolah Vokasi

Informasi Detail

Judul Seri

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No. Panggil

006.312 IGU i

Penerbit

: ., 2017

Deskripsi Fisik

xiv, 218 p. : ill.; 34 cm

Bahasa

English

ISBN/ISSN

1863-7310

Klasifikasi

NONE

Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

Ed. 1

Subjek

Database Computer Science

Info Detail Spesifik

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Pernyataan Tanggungjawab

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Versi lain/terkait

Lampiran Berkas

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