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Online Course

Big Data Analysis with Apache Spark

University of California, Berkeley via edX

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Overview

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.

Taught by

Anthony D. Joseph

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Reviews for edX's Big Data Analysis with Apache Spark Based on 43 reviews

  • 5 stars 44%
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  • 3 stars 12%
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Gregory S
by Gregory completed this course.


CS100.1x Introduction to Big Data with Apache Spark is a 5-week intro to distributed computing offered by UC Berkeley through the edX MOOC platform focused on teaching students how to perform large-scale computation using Apache Spark. The assignments use PySpark, Spark’s Python API, so some familiarity with Python programming is necessary. You don’t need prior exposure to big data or distributed computing to take the course. Grades are based on four programming labs (80%), easy comprehension questions that allow unlimited attempts (12%) and setup of the course virtual machine us…
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Martin S
by Martin completed this course, spending 4 hours a week on it and found the course difficulty to be medium.
Overall a good course, that is worthwhile spending the time on, if you want to get familiar with spark and the map-reduce programming model.

The lecture videos and quizzes are pretty lightweight, and nothing spectacular. However, I found the assignments really well structured, interesting, and informative. They use IPython notebook which I found to be a really awesome format for this kind of course and assignments.

The course is not heave on mathematics and statistics, but the assignments will challenge you to really understand the stated problems, and the map-reduce programming model, to successfully complete them.

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Anoop T
by Anoop is taking this course right now and found the course difficulty to be medium.
It was nice course. I loved it.

Good Intro PySpark API.

Nice set of Problem set.

As a part of it, if you are lucky you will get access to Databricks clouds
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Wendao L
by Wendao is taking this course right now.
Slightly disappointed by the content, not very informative. if u wanna learn more about spark, u definitely need explore more material.
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Gaurav S
by Gaurav is taking this course right now, spending 4 hours a week on it and found the course difficulty to be medium.
Lectures are very light in content and disappointing but the labs are good and do require students to investigate and complete them.
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Charlie S
by Charlie completed this course, spending 5 hours a week on it and found the course difficulty to be hard.
This is an excellent course for beginners to the world of Spark but it would be a good idea to have some programming knowledge in Python as well as basic understanding of what big data means. The problem sets are organized methodically with much explanation so even if you don't know much statistics you can still follow with the programming. I'm no statistician but managed to go through all problem sets with few mistakes. It certainly was fun on top of being educational and informative.
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