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Data science fundamentals for Python...

Paper, David.

Data science fundamentals for Python and MongoDB

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data science fundamentals for Python and MongoDBby David Paper.
Author:	Paper, David.
Published:	Berkeley, CA :Apress :2018.
Description:	xiii, 214 p. :digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Data mining.
Online resource:	http://dx.doi.org/10.1007/978-1-4842-3597-3
ISBN:	9781484235973$q(electronic bk.)

Data science fundamentals for Python and MongoDB
Paper, David.

Data science fundamentals for Python and MongoDB[electronic resource] /by David Paper. - Berkeley, CA :Apress :2018. - xiii, 214 p. :digital ;24 cm.

1. Introduction -- 2. Monte Carlo Simulation and Density Functions -- 3. Linear Algebra -- 4. Gradient Descent -- 5. Working with Data -- 6. Exploring Data.

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.

ISBN: 9781484235973$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-3597-3doiSubjects--Uniform Titles:

MongoDB.
Subjects--Topical Terms:

184440
Data mining.

LC Class. No.: QA76.9.D343 / P374 2018

Dewey Class. No.: 006.312

Data science fundamentals for Python and MongoDB
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505 0 $a 1. Introduction -- 2. Monte Carlo Simulation and Density Functions -- 3. Linear Algebra -- 4. Gradient Descent -- 5. Working with Data -- 6. Exploring Data.
520 $a 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.
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