This article was written by Prakarsh Saxena.
In a world where each of us is surrounded by data and its insights, Data Science does seem a promising field of work and research to many. Although there are many books and courses which help us dive into the area right from the scratch, it is also essential for the adepts to have a separate book containing all their commonly used terminologies and techniques suitable for their level, so that they don’t have to scavenge through the whole book or course right till the end. Also, for those wanting to switch from an R or SAS background to Python, this book – Python Data Science Handbook- will be something to grab a hold on.
About the book:
Python has grown to be in popular use amongst many researchers due to its humongous community of developers, its versatility, and also because of its powerful libraries and packages for storing, manipulating, and gaining insights from data. The book gathers its uniqueness because of capturing every important usage of various Python libraries and packages finding immense use in Data Science, namely IPython, NumPy, Pandas, Matplotlib, Scikit-Learn and other similarly related tools.
The book, written by Jake VanderPlas and published by O’Reilly, does not claim to be something which will be used to someone who wants to get into the field from the start. As the name suggests, the Handbook is targeted towards the working professionals and researchers to help them with the common usages of the libraries and methods in Python related to the field of Data Science. The readers will find it an ideal reference, which will help them tackle day- to- day issues: manipulating, transforming, cleaning, visualizing data, or in building statistical or machine learning models for better forecasting. In simple words, this is a must- have a reference for scientific and statistical computing in Python adepts, and also for those who have a command over the subject but in other languages, and want to implement the same in Python.
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