© 2019

Practical Data Science with Python 3

Synthesizing Actionable Insights from Data

  • Provides a mechanism to solidify data science related topics in a unified fashion, while treating theory and practice as equally important

  • Uses publicly available real life data-sets, that cannot be tackled without hinging on advanced data science methods and tools

  • Focuses on knowledge synthesis; how things come together in data science, and more importantly why


Table of contents

  1. Front Matter
    Pages i-xvii
  2. Ervin Varga
    Pages 1-27
  3. Ervin Varga
    Pages 29-71
  4. Ervin Varga
    Pages 73-119
  5. Ervin Varga
    Pages 121-158
  6. Ervin Varga
    Pages 159-207
  7. Ervin Varga
    Pages 209-253
  8. Ervin Varga
    Pages 255-316
  9. Ervin Varga
    Pages 317-339
  10. Ervin Varga
    Pages 341-367
  11. Ervin Varga
    Pages 369-396
  12. Ervin Varga
    Pages 397-425
  13. Ervin Varga
    Pages 427-450
  14. Back Matter
    Pages 451-462

About this book


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.


Data Science Python 3 Machine Learning Neural Networks OMG Essence Apache Spark TensorFlow Numpy Pandas Matpotlib IPython notebooks

Authors and affiliations

  1. 1.KikindaSerbia

About the authors

Ervin Varga is a Senior Member of IEEE and Professional Member of ACM. He is an IEEE Software Engineering Certified Instructor. Ervin is an owner of the software consulting company Expro I.T. Consulting, Serbia. He has an MSc in computer science, and a PhD in electrical engineering (his thesis was an application of software engineering and computer science in the domain of electrical power systems). Ervin is also a technical advisor of the open-source project Mainflux.

Bibliographic information

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