Apress | Practical Data Science With Python 3: Synthesizing Actionable Insights From Data (2019 EN)

Author: Ervin Varga
Full Title: Practical Data Science With Python 3: Synthesizing Actionable Insights From Data
Publisher: Apress; 1st ed. edition (September 7, 2019)
Year: 2019
ISBN-13: 9781484248591 (978-1-4842-4859-1), 9781484248584 (978-1-4842-4858-4)
ISBN-10: 1484248597, 1484248589
Pages: 462
Language: English
Genre: Educational: Programming
File type: EPUB (True), PDF (True), Code Files
Quality: 10/10
Price: 37.44 €


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.


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

Features:
✓ 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

Who This Book Is For:
Anyone who would like to embark into the realm of data science using Python 3.

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