By Antonio Gulli
BigData and desktop studying in Python and Spark
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Additional info for A collection of Data Science Interview Questions Solved in Python and Spark: Hands-on Big Data and Machine Learning
Note that Spark hides all the low level details to the programmer by allowing to write a distributed code which is very close to a mathematical formulation. The code adopts python lambda computation for compact representations of anonymous functions.  11. Can you provide examples for other computations in Spark? Solution The first code fragment is an example of map reduction, where we want to find the line with most words in a text. First each line is mapped into the number of words it contains.
The following code snippet shows how to plot a function. show() 21. Can you represent a graph in Python? Solution Python provides a convenient library for graph representation named Networkx , which provides support for direct / indirect graphs and multigraphs. Also many graph algorithms are already implemented in the framework. This simple code snippet computes the connected components algorithm. connected_components(G) 22. What is an Ipython notebook? Solution Ipython notebook is a convenient interface for executing Python codes directly from a web browser.
In Clustering computers learn how to partition observations in various subsets, so that each partition will be made up of similar observations according to some well-defined metric. Algorithms like K-Means and DBSCAN belong also to this class. In Density estimation computers learn how to find statistical values that describe data. Algorithms like Expectation Maximization belong also to this class. 2. Why is it important to have a robust set of metrics for machine learning? Solution Any machine learning technique should be evaluated by using metrics for analytically assessing the quality of results.