Data Science Fundamentals with Python and MongoDB” by David Paper is a practical, example-driven guide designed to build strong foundational skills in data science. The book covers essential mathematical and programming concepts—including Monte Carlo simulations, stochastic algorithms, linear algebra with vectors and matrices, gradient descent, principal component analysis, data wrangling, cleansing, visualization, and problem-solving—while integrating Python coding examples and MongoDB/JSON for handling data. Perfect for beginners and enthusiasts, it emphasizes hands-on learning with complete, concise code snippets to prepare readers for more advanced machine learning applications and a career in data science.
Key Features:
- Example-driven approach with complete, ready-to-run Python code examples
- Covers core fundamentals: Monte Carlo & stochastic simulations, linear algebra, gradient descent, and PCA
- Practical focus on data wrangling, cleansing, visualization, and problem-solving
- Integration of MongoDB and JSON for real-world NoSQL data handling
- Self-contained content including required math, statistics, and programming basics
- Ideal for novices entering data science or those strengthening foundational knowledge