From 0 to 1: Spark for Data Science with PythonQuickStart
From 0 to 1: Spark for Data Science with Python
Get your data to fly using Spark for analytics, machine learning and data science
Let’s parse that.
What's Spark? If you are an analyst or a data scientist, you're used to having multiple systems for working with data. SQL, Python, R, Java, etc. With Spark, you have a single engine where you can explore and play with large amounts of data, run machine learning algorithms and then use the same system to productionize your code.
Analytics: Using Spark and Python you can analyze and explore your data in an interactive environment with fast feedback. The course will show how to leverage the power of RDDs and Dataframes to manipulate data with ease.
Machine Learning and Data Science : Spark's core functionality and built-in libraries make it easy to implement complex algorithms like Recommendations with very few lines of code. We'll cover a variety of datasets and algorithms including PageRank, MapReduce and Graph datasets.
Who should attend?
- Analysts who want to leverage Spark for analyzing interesting datasets
- Data Scientists who want a single engine for analyzing and modelling data as well as productionizing it.
- Engineers who want to use a distributed computing engine for batch or stream processing or both
- The course assumes knowledge of Python. You can write Python code directly in the PySpark shell. If you already have IPython Notebook installed, we'll show you how to configure it for Spark
- For the Java section, we assume basic knowledge of Java. An IDE which supports Maven, like IntelliJ IDEA/Eclipse would be helpful
- All examples work with or without Hadoop. If you would like to use Spark with Hadoop, you'll need to have Hadoop installed (either in pseudo-distributed or cluster mode).
- Music Recommendations using Alternating Least Squares and the Audioscrobbler dataset
- Dataframes and Spark SQL to work with Twitter data
- Using the PageRank algorithm with Google web graph dataset
- Using Spark Streaming for stream processing
- Working with graph data using the Marvel Social network dataset
Why choose QuickStart?
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QuickStart exists to create world-class technologists by personalizing and individualizing training to address the massive skills gap in the IT industry. Through 20 years of research and data analysis, we’ve learned that a modern learner prefers to learn through multiple...
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