Data Science Fundamentals for Python and MongoDB
Apress (Verlag)
978-1-4842-3596-6 (ISBN)
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.
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.
1. Introduction.- 2. Monte Carlo Simulation and Density Functions.- 3. Linear Algebra.- 4. Gradient Descent.- 5. Working with Data.- 6. Exploring Data.
| Erscheinungsdatum | 15.05.2018 |
|---|---|
| Zusatzinfo | 117 Illustrations, black and white; XIII, 214 p. 117 illus. |
| Verlagsort | Berkley |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Mathematik / Informatik ► Informatik ► Netzwerke | |
| Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge | |
| Mathematik / Informatik ► Informatik ► Web / Internet | |
| Schlagworte | data cleansing • Data Science • Data Visualization • data wrangling • Gradient descent • heat map • JSON • linear algebra • MongoDB • Monte Carlo simulation • Normal distribution • NoSQL • Python NumPy Library • Python Pandas Library • randomness • Simulation • Stochastic Simulation • Uniform distribution • Vector and Matrix Math |
| ISBN-10 | 1-4842-3596-7 / 1484235967 |
| ISBN-13 | 978-1-4842-3596-6 / 9781484235966 |
| Zustand | Neuware |
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