Practical Data Science with Python 3
Synthesizing Actionable Insights from Data
Authors: Varga, Ervin
Download source code Free Preview- 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
Buy this book
- 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.
What You'll Learn- Play the role of a data scientist when completing increasingly challenging exercises using Python 3
- Work work with proven data science techniques/technologies
- Review scalable software engineering practices to ramp up data analysis abilities in the realm of Big Data
- Apply theory of probability, statistical inference, and algebra to understand the data science practices
Anyone who would like to embark into the realm of data science using Python 3.
- 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.
- Table of contents (12 chapters)
-
-
Introduction to Data Science
Pages 1-27
-
Data Engineering
Pages 29-71
-
Software Engineering
Pages 73-119
-
Documenting Your Work
Pages 121-158
-
Data Processing
Pages 159-207
-
Table of contents (12 chapters)
Bibliographic Information
- Bibliographic Information
-
- Book Title
- Practical Data Science with Python 3
- Book Subtitle
- Synthesizing Actionable Insights from Data
- Authors
-
- Ervin Varga
- Copyright
- 2019
- Publisher
- Apress
- Copyright Holder
- Ervin Varga
- Distribution Rights
- Apress Standard Distribution
- eBook ISBN
- 978-1-4842-4859-1
- DOI
- 10.1007/978-1-4842-4859-1
- Softcover ISBN
- 978-1-4842-4858-4
- Edition Number
- 1
- Number of Pages
- XVII, 462
- Number of Illustrations
- 94 b/w illustrations
- Topics
