A collection of Data Science Interview Questions Solved in by Antonio Gulli

By Antonio Gulli

BigData and desktop studying in Python and Spark

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Pretty simple: one single line of code stays here for something which requires hundreds of lines in other parallel paradigms such as Hadoop. Spark supports two types of operations: transformations, which create a new RDD dataset from an existing one, and actions, which return a value to the driver program after running a computation on the dataset. All transformations in Spark are lazy because the computation is postponed as much as possible until the results are really needed by the program. This allows Spark to run efficiently – for example the compiler can realize that an RDD created through map will be used in a reduce and return only the result of the reduce to the driver, rather than the larger mapped dataset.

Can you provide an example of features extraction? Solution Code 5. What is a training set, a validation set, a test set and a gold set in supervised and unsupervised learning? Solution 6. What is a Bias - Variance tradeoff? Solution 7. What is a cross-validation and what is an overfitting? Solution Code 8. Why are vectors and norms used in machine learning? Solution Code 9. What are Numpy, Scipy and Spark essential datatypes? Solution Code 10. Can you provide an example for Map and Reduce in Spark?

So the rule of thumb is in what Galileo already said many centuries ago: “Simplicity is the ultimate sophistication”. Pick your algorithm carefully and spend a lot of time in investigating your data and in creating meaningful summaries with appropriate feature engineering. Real world objects are complex and features are used to analytically represent those objects. From one hand this representation has an inherent error which can be reduced by carefully selecting a right set of representatives.

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