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Data science fundamentals for Python and MongoDB
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 v...
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| Formaat: | Printed Book |
| Gepubliceerd in: |
Apress
2018
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| Onderwerpen: |
Inhoudsopgave:
- Intro; Table of Contents; About the Author; About the Technical Reviewer; Acknowledgments; Chapter 1: Introduction; Python Fundamentals; Functions and Strings; Lists, Tuples, and Dictionaries; Reading and Writing Data; List Comprehension; Generators; Data Randomization; MongoDB and JSON; Visualization; Chapter 2: Monte Carlo Simulation and Density Functions; Stock Simulations; What-If Analysis; Product Demand Simulation; Randomness Using Probability and Cumulative Density Functions; Chapter 3: Linear Algebra; Vector Spaces; Vector Math; Matrix Math; Basic Matrix Transformations. Pandas Matrix Applications; Chapter 4: Gradient Descent; Simple Function Minimization (and Maximization); Sigmoid Function Minimization (and Maximization); Euclidean Distance Minimization Controlling for Step Size; Stabilizing Euclidean Distance Minimization with Monte Carlo Simulation; Substituting a NumPy Method to Hasten Euclidean Distance Minimization; Stochastic Gradient Descent Minimization and Maximization; Chapter 5: Working with Data; One-Dimensional Data Example; Two-Dimensional Data Example; Data Correlation and Basic Statistics; Pandas Correlation and Heat Map Examples. Various Visualization Examples; Cleaning a CSV File with Pandas and JSON; Slicing and Dicing; Data Cubes; Data Scaling and Wrangling; Chapter 6: Exploring Data; Heat Maps; Principal Component Analysis; Speed Simulation; Big Data; Twitter; Web Scraping; Index.