Beginning Data Exploration and Analysis with Apache Spark
80% of a data scientist's job is data preparation. This course is all about data preparation i.e. cleaning, transforming, summarizing data using Spark.
What you'll learn
Data preparation is a staple task for any data professional, whether you just want to explore data or develop sophisticated Machine Learning models. Spark is an engine that helps do this in a very intuitive way, using functional constructs that abstract the user from all the messiness of working with large datasets. In this course, Beginning Data Exploration and Analysis with Apache Spark, you'll go through exploratory data analysis and data munging with Spark, step-by-step. First, you'll explore RDDs and functional constructs that make processing in Spark extremely intuitive. Next, you'll discover how to transform and clean unstructured data. Finally, you'll learn how to summarize data along dimensions and how to model relationships to build co-occurrence networks. By the end of this course, you'll be able to use Spark to transform data in any way that you would like.
Table of contents
- Analyzing Crime in New York City 5m
- Programming in the Functional Paradigm 4m
- Applying Functional Constructs to Transform Datasets 5m
- Filtering Rows 2m
- Transforming Records to Extract Fields 4m
- Identifying and Filtering Missing Values 4m
- Identifying and Filtering Anomalies 5m
- Summarizing and Visualizing Crime in NYC 5m