Data Science Fundamentals for Python and MongoDB (eBook)

David Paper (Autor)

eBook Download: PDF

2018 | 1st ed.
XIII, 214 Seiten
Apress (Verlag)
978-1-4842-3597-3 (ISBN)

Infos

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 fundamentals that 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.

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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 "e;rocky"e; at best. The coding examples in this book are concise, accurate, and complete, and perfectly complement the data science concepts introduced. What You'll LearnPrepare for a career in data scienceWork with complex data structures in PythonSimulate with Monte Carlo and Stochastic algorithmsApply linear algebra using vectors and matricesUtilize complex algorithms such as gradient descent and principal component analysisWrangle, cleanse, visualize, and problem solve with dataUse MongoDB and JSON to work with dataWho This Book Is ForThe 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.

Inhalt

Table of Contents 5
About the Author 8
About the Technical Reviewer 9
Acknowledgments 10
Chapter 1: Introduction 11
Python Fundamentals 13
Functions and Strings 13
Lists, Tuples, and Dictionaries 16
Reading and Writing Data 22
List Comprehension 25
Generators 28
Data Randomization 32
MongoDB and JSON 37
Visualization 44
Chapter 2: Monte Carlo Simulation and Density Functions 47
Stock Simulations 47
What-If Analysis 52
Product Demand Simulation 54
Randomness Using Probability and Cumulative Density Functions 62
Chapter 3: Linear Algebra 76
Vector Spaces 76
Vector Math 77
Matrix Math 84
Basic Matrix Transformations 93
Pandas Matrix Applications 97
Chapter 4: Gradient Descent 106
Simple Function Minimization (and Maximization) 106
Sigmoid Function Minimization (and Maximization) 113
Euclidean Distance Minimization Controlling for Step Size 118
Stabilizing Euclidean Distance Minimization with Monte Carlo Simulation 121
Substituting a NumPy Method to Hasten Euclidean Distance Minimization 124
Stochastic Gradient Descent Minimization and Maximization 127
Chapter 5: Working with Data 138
One-Dimensional Data Example 138
Two-Dimensional Data Example 141
Data Correlation and Basic Statistics 144
Pandas Correlation and Heat Map Examples 147
Various Visualization Examples 150
Cleaning a CSV File with Pandas and JSON 155
Slicing and Dicing 157
Data Cubes 158
Data Scaling and Wrangling 163
Chapter 6: Exploring Data 175
Heat Maps 175
Principal Component Analysis 178
Speed Simulation 187
Big Data 190
Twitter 209
Web Scraping 213
Index 218

Produkt-Details

• Erscheint lt. Verlag: 10.5.2018
• Zusatzinfo: XIII, 214 p. 117 illus.
• Verlagsort: Berkeley
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Datenbanken
• Themenwelt: Mathematik / Informatik ► Informatik ► Netzwerke
• Themenwelt: Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge
• Themenwelt: 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: 1-4842-3597-5 / 1484235975
• ISBN-13: 978-1-4842-3597-3 / 9781484235973