Data Science Fundamentals for Python and MongoDB

  • David Paper

Table of contents

  1. Front Matter
    Pages i-xiii
  2. David Paper
    Pages 1-36
  3. David Paper
    Pages 67-96
  4. David Paper
    Pages 97-128
  5. David Paper
    Pages 129-165
  6. David Paper
    Pages 167-209
  7. Back Matter
    Pages 211-214

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. 

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

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

Authors and affiliations

  • David Paper
    • 1
  1. 1.Apt 3LoganUSA

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
  • Print ISBN 978-1-4842-3596-6
  • Online ISBN 978-1-4842-3597-3
  • About this book
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