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.

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