synthesizing actionable insights from Data Varga, Ervin creator text Electronic books. xxu Berkeley Apress 2019 1st ed. monographic eng Xvii, 462p. 23.5 cm. 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. 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. By Ervin Varga. Requires an SPL library card. Mode of access: World Wide Web. Estimating the edge betweenness centrality Partitioning the model into a bipartite graph QA76.73.P98 005.76 VARE 9781484248584 https://ezproxy.spl.org/login?url=https://learning.oreilly.com/library/view/~/9781484248591/?ar https://ezproxy.spl.org/login?url=https://learning.oreilly.com/library/view/~/9781484248591/?ar Requires an SPL library card. 190907 20220419114800.0 ssj0002240198