Paper Data Science Fundamentals for Python and MongoDB

1. ed
ISBN: 978-1-4842-3597-3
Verlag: Apress
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 221 Seiten

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 LearnPrepare 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.

Autoren/Hrsg.

Paper, David

Inhaltsverzeichnis

1;Table of Contents;5
2;About the Author;8
3;About the Technical Reviewer;9
4;Acknowledgments;10
5;Chapter 1: Introduction;11
5.1;Python Fundamentals;13
5.2;Functions and Strings;13
5.3;Lists, Tuples, and Dictionaries;16
5.4;Reading and Writing Data;22
5.5;List Comprehension;25
5.6;Generators;28
5.7;Data Randomization;32
5.8;MongoDB and JSON;37
5.9;Visualization;44
6;Chapter 2: Monte Carlo Simulation and Density Functions;47
6.1;Stock Simulations;47
6.2;What-If Analysis;52
6.3;Product Demand Simulation;54
6.4;Randomness Using Probability and Cumulative Density Functions;62
7;Chapter 3: Linear Algebra;76
7.1;Vector Spaces;76
7.2;Vector Math;77
7.3;Matrix Math;84
7.4;Basic Matrix Transformations;93
7.5;Pandas Matrix Applications;97
8;Chapter 4: Gradient Descent;106
8.1;Simple Function Minimization (and Maximization);106
8.2;Sigmoid Function Minimization (and Maximization);113
8.3;Euclidean Distance Minimization Controlling for Step Size;118
8.4;Stabilizing Euclidean Distance Minimization with Monte Carlo Simulation;121
8.5;Substituting a NumPy Method to Hasten Euclidean Distance Minimization;124
8.6;Stochastic Gradient Descent Minimization and Maximization;127
9;Chapter 5: Working with Data;138
9.1;One-Dimensional Data Example;138
9.2;Two-Dimensional Data Example;141
9.3;Data Correlation and Basic Statistics;144
9.4;Pandas Correlation and Heat Map Examples;147
9.5;Various Visualization Examples;150
9.6;Cleaning a CSV File with Pandas and JSON;155
9.7;Slicing and Dicing;157
9.8;Data Cubes;158
9.9;Data Scaling and Wrangling;163
10;Chapter 6: Exploring Data;175
10.1;Heat Maps;175
10.2;Principal Component Analysis;178
10.3;Speed Simulation;187
10.4;Big Data;190
10.5;Twitter;209
10.6;Web Scraping;213
11;Index;218