What does this mean?Highlights
Quality
Final two chapters are devoted to social network analysis with the networkx package and data visualization with the matplotlib ... Read More
The chapter ends with short examples of doing basic operations of natural language processing with the nltk package and data clustering
Competitiveness
In the last year my data set has ballooned at the rate of 1tb every two months and will soon exceed the handling capacity of my... Read More
Overview
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first, two and new. - Our engine has profiled the reviewer patterns and has determined that there is minimal deception involved.
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- Our engine has discovered that over 90% high quality reviews are present.
- This product had a total of 8 reviews as of our last analysis date on Sep 15 2021.
Helpful InsightsBETA
Posted by a reviewer on Amazon
The book covers fundamentals of data science
Posted by a reviewer on Amazon
Code for the book is available from the publisher
Posted by a reviewer on Amazon
I like the table on graph examples type node and edges
Posted by a reviewer on Amazon
As all code examples in the book are run in ipython notebook special attention is paid to a short but comprehensive introduction into ipython itself
Posted by a reviewer on Amazon
Data sets used in the book are described too
Posted by a reviewer on Amazon
After advising on installation of python and its packages the book guides readers towards fast and easy data loading from a file including the case when the entire data set cannot be loaded during one read in the memory and the solution offered is to load it in chunks by using pandas
Posted by a reviewer on Amazon
Furthermore answers to the following problems are provided how to deal with erroneous records how to treat categorical and text data what are useful data cleansing and transformation operations implemented in pandas how to use the optimized data structures numpy arrays and what operations on them can be done
Posted by a reviewer on Amazon
Once data is loaded and converted to a suitable representation the book then spends a chapter on the general data science pipeline that can be implemented with scikitlearn
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