Scala has emerged as an important tool for performing various data analysis tasks efficiently. This video will help you leverage popular Scala libraries and tools to perform core data analysis tasks with ease.
This course will give you everything that you need to perform data analysis with Scala libraries. You will master loading raw datasets with Spark, and perform exploratory data analysis on them via plotting. Along the way you will learn what Spark has to offer when it comes to transforming datasets and how you can build a statistical model of a dataset with Spark.
About the Author
Anatolii Kmetiuk has been working with Scala-based technologies for four years. He has experience in Deep Learning models for text processing.
He is interested in Category Theory and Type-level programming in Scala. Another field of interest is Chaos and Complexity Theory and Artificial Life, and ways to implement them in programming languages.
We need a data set to practice the skills learned in this course. We download the Houses Prices dataset from Kaggle for this.
Before proceeding to load the data, we need to understand how Spark represents and handles it. This theoretical part covers it.
Preprocess the data before feeding it to a ML algorithm. This video describes how to do that with standard SQL/Collections methods.
SparkSQL operations are powerful, but SparkML supports some common ML operations out of the box. Learning them may greatly reduce the work to be done.
Before proceeding to concrete examples of using SparkML, we need to understand its structure.
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