Data Science Fundamentals for Python and MongoDB
Autor David Paperen Limba Engleză Paperback – 11 mai 2018
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.
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
Preț: 161.19 lei
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30.86€ • 31.74$ • 25.60£
30.86€ • 31.74$ • 25.60£
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Specificații
ISBN-13: 9781484235966
ISBN-10: 1484235967
Pagini: 140
Ilustrații: XIII, 214 p. 117 illus.
Dimensiuni: 155 x 235 x 16 mm
Greutate: 0.33 kg
Ediția:1st ed.
Editura: Apress
Colecția Apress
Locul publicării:Berkeley, CA, United States
ISBN-10: 1484235967
Pagini: 140
Ilustrații: XIII, 214 p. 117 illus.
Dimensiuni: 155 x 235 x 16 mm
Greutate: 0.33 kg
Ediția:1st ed.
Editura: Apress
Colecția Apress
Locul publicării:Berkeley, CA, United States
Cuprins
1. Introduction.- 2. Monte Carlo Simulation and Density Functions.- 3. Linear Algebra.- 4. Gradient Descent.- 5. Working with Data.- 6. Exploring Data.
Notă biografică
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.
Textul de pe ultima copertă
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.
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
Caracteristici
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