rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Practical Data Science with Python 3
    sub title: Synthesizing Actionable Insights from Data
  name:
    name part: Varga, Ervin.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxu
    date issued: 2019
    edition: 1st ed. 2019.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVII, 462 p. 94 illus. online resource.
  abstract: 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.
  table of 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.
  note: by Ervin Varga.
  subject:
    topic: Python (Computer program language)
  subject:
    topic: Big data
  subject:
    topic: Open source software
  subject:
    topic: Computer programming
  subject:
    topic: Python
  subject:
    topic: Big Data
  subject:
    topic: Open Source
  classification: QA76.73.P98
  classification: 005.133
  related item:
    title info:
      title: Springer eBooks
  identifier: 9781484248591
  identifier: https://doi.org/10.1007/978-1-4842-4859-1
  record info:
    record creation date: 190907
    record change date: 20190908141049.0
    record identifier: 978-1-4842-4859-1