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Learning Data Science is the first book to cover foundational skills in both programming and statistics that encompass the entire data science lifecycle: the process of collecting, wrangling, analyzing, and drawing conclusions from data.
With this short but thorough resource, data scientists and Python programmers will learn how the Dask open source library for parallel computing provides APIs that make it easy to parallelize PyData libraries including NumPy, pandas, and scikit-learn.
In Working Beyond Borders: GIS for Geospatial Collaboration, see how government, industries, and others, are using location intelligence and... Læs mere
This accessible guide to data modeling introduces basic probabilistic concepts, gradually building toward state-of-the art data modeling... Læs mere
The text discusses various modalities of data and provides aggregated solutions using cloud. It includes the fundamentals of neural... Læs mere
This book discusses a new process mining method along with a detailed comparison between different techniques that provide a complete vision of the process of data acquisition, data analysis, and data prediction.
This book is designed to equip students to navigate through MS Excel and basic data computation methods which are essential tools in research or professional settings and in classrooms.
This book is designed to equip students to navigate through MS Excel and basic data computation methods which are essential tools in research or professional settings and in classrooms.
Ideal for current and aspiring data scientists, this book introduces you to doing data science with R and RStudio, as well as the... Læs mere