Materias: “…Data mining…”

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Materias: “…Web usage mining…”

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Tabla de Contenidos: “…Filter out all but the TMIN entries -- Create (station ID, temperature) key/value pairs -- Find minimum temperature by station ID -- Collect and print results -- Running the minimum temperature example and modifying it for maximums -- Examining the min-temperatures script -- Running the script -- Running the maximum temperature by location example -- Counting word occurrences using flatmap() -- Map versus flatmap -- Map () -- Flatmap () -- Code sample - count the words in a book -- Improving the word-count script with regular expressions -- Text normalization -- Examining the use of regular expressions in the word-count script -- Running the code -- Sorting the word count results -- Step 1 - Implement countByValue() the hard way to create a new RDD -- Step 2 - Sort the new RDD -- Examining the script -- Running the code -- Find the total amount spent by customer -- Introducing the problem -- Strategy for solving the problem -- Useful snippets of code -- Check your results and sort them by the total amount spent -- Check your sorted implementation and results against mine -- Summary -- Chapter 3: Advanced Examples of Spark Programs -- Finding the most popular movie -- Examining the popular-movies script -- Getting results -- Using broadcast variables to display movie names instead of ID numbers -- Introducing broadcast variables -- Examining the popular-movies-nicer.py script -- Getting results -- Finding the most popular superhero in a social graph -- Superhero social networks -- Input data format -- Strategy -- Running the script - discover who the most popular superhero is -- Mapping input data to (hero ID, number of co-occurrences) per line -- Adding up co-occurrence by hero ID -- Flipping the (map) RDD to (number, hero ID) -- Using max() and looking up the name of the winner -- Getting results…”

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Materias: “…Data mining…”

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