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.
| About the Author |
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ix | |
| About the Technical Reviewer |
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xi | |
| Acknowledgments |
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3 | (1) |
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3 | (3) |
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Lists, Tuples, and Dictionaries |
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6 | (6) |
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12 | (3) |
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15 | (3) |
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18 | (4) |
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22 | (5) |
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27 | (7) |
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34 | (3) |
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Chapter 2 Monte Carlo Simulation and Density Functions |
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37 | (30) |
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37 | (5) |
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42 | (2) |
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Product Demand Simulation |
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44 | (8) |
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Randomness Using Probability and Cumulative Density Functions |
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52 | (15) |
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67 | (30) |
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67 | (1) |
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68 | (7) |
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75 | (9) |
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Basic Matrix Transformations |
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84 | (4) |
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Pandas Matrix Applications |
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88 | (9) |
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Chapter 4 Gradient Descent |
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97 | (32) |
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Simple Function Minimization (and Maximization) |
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97 | (7) |
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Sigmoid Function Minimization (and Maximization) |
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104 | (5) |
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Euclidean Distance Minimization Controlling for Step Size |
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109 | (3) |
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Stabilizing Euclidean Distance Minimization with Monte Carlo Simulation |
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112 | (3) |
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Substituting a NumPy Method to Hasten Euclidean Distance Minimization |
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115 | (3) |
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Stochastic Gradient Descent Minimization and Maximization |
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118 | (11) |
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Chapter 5 Working with Data |
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129 | (38) |
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One-Dimensional Data Example |
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129 | (3) |
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Two-Dimensional Data Example |
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132 | (3) |
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Data Correlation and Basic Statistics |
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135 | (3) |
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Pandas Correlation and Heat Map Examples |
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138 | (3) |
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Various Visualization Examples |
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141 | (5) |
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Cleaning a CSV File with Pandas and JSON |
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146 | (2) |
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148 | (1) |
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149 | (5) |
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Data Scaling and Wrangling |
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154 | (13) |
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167 | (44) |
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167 | (3) |
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Principal Component Analysis |
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170 | (9) |
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179 | (3) |
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182 | (19) |
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201 | (4) |
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205 | (6) |
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211 | |
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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