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
- Book Title Practical Data Science with Python 3
- Book Subtitle Synthesizing Actionable Insights from Data
- DOI https://doi.org/10.1007/978-1-4842-4859-1
- Copyright Information Ervin Varga 2019
- Publisher Name Apress, Berkeley, CA
- eBook Packages Professional and Applied Computing Professional and Applied Computing (R0)
- Softcover ISBN 978-1-4842-4858-4
- eBook ISBN 978-1-4842-4859-1
- Edition Number 1
- Number of Pages XVII, 462
- Number of Illustrations 94 b/w illustrations, 0 illustrations in colour
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