Data Science Fundamentals for Python and MongoDB - Info and Reading Options

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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

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