Practical Data Science with Python 3 : Synthesizing Actionable Insights from Data / by Ervin Varga.

Format:

Book

Author/Creator:

Varga, Ervin., Author.

Language:

English

Subjects (All):

Python (Computer program language).

Big data.

Open source software.

Python.

Big Data.

Open Source.

Local Subjects:

Python.

Big Data.

Open Source.

Physical Description:

1 online resource (XVII, 462 p. 94 illus.)

Edition:

1st ed. 2019.

Other Title:

Practical data science with Python three

Place of Publication:

Berkeley, CA : Apress : Imprint: Apress, 2019.

System Details:

text file

Summary:

Gain insight into essential data science skills in a holistic manner using data engineering and associated scalable computational methods. This book covers the most popular Python 3 frameworks for both local and distributed (in premise and cloud based) processing. Along the way, you will be introduced to many popular open-source frameworks, like, SciPy, scikitlearn, Numba, Apache Spark, etc. The book is structured around examples, so you will grasp core concepts via case studies and Python 3 code. As data science projects gets continuously larger and more complex, software engineering knowledge and experience is crucial to produce evolvable solutions. You'll see how to create maintainable software for data science and how to document data engineering practices. This book is a good starting point for people who want to gain practical skills to perform data science. All the code will be available in the form of IPython notebooks and Python 3 programs, which allow you to reproduce all analyses from the book and customize them for your own purpose. You'll also benefit from advanced topics like Machine Learning, Recommender Systems, and Security in Data Science. Practical Data Science with Python will empower you analyze data, formulate proper questions, and produce actionable insights, three core stages in most data science endeavors.

Contents:

Chapter 1.Introduction to Data Science

Chapter 2.Data Acquisition

Chapter 3.Basic Data Processing

Chapter 4.Documenting Work

Chapter 5.Transformation and Packaging of Data

Chapter 6.Visualization

Chapter 7.Prediction and Inference

Chapter 8.Network Analysis

Chapter 9.Data Science Process Engineering

Chapter 10. Multi-agent Systems, Game Theory and Machine Learning

Chapter 11. Probabilistic Graphical Models

Chapter 12. Security in Data Science.

Notes:

Includes bibliographical references.

ISBN:

9781484248591

1484248597

OCLC:

1123173052