• Length:
    4 Weeks
  • Effort:
    5–10 hours per week
  • Price:

    FREE
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  • Institution
  • Subject:
  • Level:
    Intermediate
  • Language:
    English
  • Video Transcript:
    English

Prerequisites

Programming background and experience with Python required. All exercises will use PySpark (part of Apache Spark). Previous experience with Spark equivalent to CS105x: Introduction to Spark required.

About this course

Organizations use their data to support and influence decisions and build data-intensive products and services, such as recommendation, prediction, and diagnostic systems. The collection of skills required by organizations to support these functions has been grouped under the term ‘data science’.

This statistics and data analysis course will attempt to articulate the expected output of data scientists and then teach students how to use PySpark (part of Spark) to deliver against these expectations. The course assignments include log mining, textual entity recognition, and collaborative filtering exercises that teach students how to manipulate data sets using parallel processing with PySpark.

This course covers advanced undergraduate-level material. It requires a programming background and experience with Python (or the ability to learn it quickly). All exercises will use PySpark (the Python API for Spark), and previous experience with Spark equivalent to Introduction to Apache Spark, is required.

What you'll learn

  • How to use Apache Spark to perform data analysis
  • How to use parallel programming to explore data sets
  • Apply log mining, textual entity recognition and collaborative filtering techniques to real-world data questions

Meet your instructors

Anthony D. Joseph
Professor in Electrical Engineering and Computer Science
University of California, Berkeley

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Who can take this course?

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