Data Science Fundamentals for Python and M...

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Beschreibung

Build the foundational data science skills necessary to work with and better understand complex data science algorithms. This example-driven book provides complete Python coding examples to complement and clarify data science concepts, and enrich the learning experience. Coding examples include visualizations whenever appropriate. The book is a necessary precursor to applying and implementing machine learning algorithms.

The book is self-contained. All of the math, statistics, stochastic, and programming skills required to master the content are covered. In-depth knowledge of object-oriented programming isn¿t required because complete examples are provided and explained.

Data Science Fundamentals with Python and MongoDB is an excellent starting point for those interested in pursuing a career in data science. Like any science, the fundamentals of data science are a prerequisite to competency. Without proficiency in mathematics, statistics, data manipulation, and coding, the path to success is ¿rocky¿ at best. The coding examples in this book are concise, accurate, and complete, and perfectly complement the data science concepts introduced.

What You'll Learn

Prepare for a career in data science

Work with complex data structures in Python

Simulate with Monte Carlo and Stochastic algorithms

Apply linear algebra using vectors and matrices

Utilize complex algorithms such as gradient descent and principal component analysis

Wrangle, cleanse, visualize, and problem solve with data

Use MongoDB and JSON to work with data

Who This Book Is For

The novice yearning to break into the data science world, and the enthusiast looking to enrich, deepen, and develop data science skills through mastering the underlying fundamentalsthat are sometimes skipped over in the rush to be productive. Some knowledge of object-oriented programming will make learning easier.

What You'll Learn

Who This Book Is For

Über den Autor

Dr. David Paper is a full professor at Utah State University in the Management Information Systems department. He wrote the book Web Programming for Business: PHP Object-Oriented Programming with Oracle and he has over 70 publications in refereed journals such as Organizational Research Methods, Communications of the ACM, Information & Management, Information Resource Management Journal, Communications of the AIS, Journal of Information Technology Case and Application Research, and Long Range Planning. He has also served on several editorial boards in various capacities, including associate editor. Besides growing up in family businesses, Dr. Paper has worked for Texas Instruments, DLS, Inc., and the Phoenix Small Business Administration. He has performed IS consulting work for IBM, AT&T, Octel, Utah Department of Transportation, and the Space Dynamics Laboratory. Dr. Paper's teaching and research interests include data science, process reengineering, object-oriented programming, electronic customer relationship management, change management, e-commerce, and enterprise integration.

Zusammenfassung

Takes an example-driven approach to learning

Has everything you need in terms of content and coding to gain fundamental data science skills

A focused and easy-to-read fundamentals book

Inhaltsverzeichnis

1. Introduction.- 2. Monte Carlo Simulation and Density Functions.- 3. Linear Algebra.- 4. Gradient Descent.- 5. Working with Data.- 6. Exploring Data.

Details

Erscheinungsjahr:	2018
Fachbereich:	Datenkommunikation, Netze & Mailboxen
Genre:	Informatik
Rubrik:	Naturwissenschaften & Technik
Medium:	Taschenbuch
Seiten:	228
Inhalt:	xiii 214 S. 117 s/w Illustr. 214 p. 117 illus.
ISBN-13:	9781484235966
ISBN-10:	1484235967
Sprache:	Englisch
Herstellernummer:	978-1-4842-3596-6
Ausstattung / Beilage:	Paperback
Einband:	Kartoniert / Broschiert
Autor:	Paper, David
Auflage:	1st ed.
Hersteller:	APRESS
Maße:	235 x 155 x 13 mm
Von/Mit:	David Paper
Erscheinungsdatum:	11.05.2018
Gewicht:	0,353 kg

preigu-id: 111688735