## About this book

### Introduction

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.

*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

### Keywords

Data Science Simulation Monte Carlo Simulation Linear Algebra Vector and Matrix Math Stochastic Simulation Randomness Gradient Descent Data Wrangling Data Cleansing Heat Map MongoDB NoSQL JSON Python Pandas Library Python NumPy Library Data Visualization Uniform Distribution Normal Distribution

### Bibliographic information

- DOI https://doi.org/10.1007/978-1-4842-3597-3
- Copyright Information David Paper 2018
- Publisher Name Apress, Berkeley, CA
- eBook Packages Professional and Applied Computing Professional and Applied Computing (R0)
- Print ISBN 978-1-4842-3596-6
- Online ISBN 978-1-4842-3597-3
- Buy this book on publisher's site