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According to Wikipedia, Statistics is a branch of mathematics dealing with data collection, organization, analysis, interpretation and presentation. It is about building from collected data, a model that can enable humans to describe, analyze and infer event happening around. Statistics is in itself a conduit to the field of Machine Learning and AI.
In this Hackerday, we will go through the basis of statistics and see how Spark enables us to perform statistical operations like descriptive and inferential statistics over the very large dataset.
No knowledge of statistics is assumed in this session. Every concept will be discussed ground up and put to practice on the airline on-time performance dataset. We will conclude the session by introducing a number of machine learning algorithms available in MLlib.
In this PySpark project, you will simulate a complex real-world data pipeline based on messaging. This project is deployed using the following tech stack - NiFi, PySpark, Hive, HDFS, Kafka, Airflow, Tableau and AWS QuickSight.
In this big data spark project, we will do Twitter sentiment analysis using spark streaming on the incoming streaming data.
In this project, we will use complex scenarios to make Spark developers better to deal with the issues that come in the real world.