Big Data Analysis with Spark - University of California

edX
4.8
5 reviews
  • This course is vert importante for companys because they nées statistcals informations and tols for save this informations.
    ← | →
  • This ought to have been published before CS120x: Distributed Machine Learning with Apache Spark.
    ← | →
  • A considerable measure of duplicacy with the 2 different courses of the xSerie. I would not prompt taking this course on the off chance that you took them. The remainder of the 4 weeks comprises of just 20 minutes of video clarifying extremely essential statistical ideas.
    ← | →

Course

Online

Free

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Description

  • Type

    Course

  • Methodology

    Online

  • Duration

    4 Weeks

  • Start date

    Different dates available

Learn how to apply data science techniques using parallel programming in Spark to explore big data. With an apprenticeship you earn while you learn, you gain recognized qualifications, job specific skills and knowledge and this helps you stand out in the job market.With this course you earn while you learn, you gain recognized qualifications, job specific skills and knowledge and this helps you stand out in the job market.

Facilities

Location

Start date

Online

Start date

Different dates availableEnrolment now open

About this course

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.

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Reviews

4.8
excellent
  • This course is vert importante for companys because they nées statistcals informations and tols for save this informations.
    ← | →
  • This ought to have been published before CS120x: Distributed Machine Learning with Apache Spark.
    ← | →
  • A considerable measure of duplicacy with the 2 different courses of the xSerie. I would not prompt taking this course on the off chance that you took them. The remainder of the 4 weeks comprises of just 20 minutes of video clarifying extremely essential statistical ideas.
    ← | →
100%
4.3
fantastic

Course rating

Recommended

Centre rating

Amar Ammouri

5.0
01/03/2017
What I would highlight: This course is vert importante for companys because they nées statistcals informations and tols for save this informations.
What could be improved: Mathematicals knowlages and informatique.
Would you recommend this course?: Yes

Shankar K

5.0
24/12/2016
What I would highlight: This ought to have been published before CS120x: Distributed Machine Learning with Apache Spark.
What could be improved: Everything was positive.
Would you recommend this course?: Yes

Ex-student

5.0
23/12/2016
What I would highlight: A considerable measure of duplicacy with the 2 different courses of the xSerie. I would not prompt taking this course on the off chance that you took them. The remainder of the 4 weeks comprises of just 20 minutes of video clarifying extremely essential statistical ideas.
What could be improved: Nothing.
Would you recommend this course?: Yes

Ex-student

4.5
22/12/2016
What I would highlight: Incredible course association, particularly the harmony amongst hypothesis and practice. A few undertakings were too simple and some were not clear at in the first place, but rather piazza look normally made a difference. I consider this is a decent pyspark instructional exercise with clarification of start key elements.
What could be improved: N/A.
Would you recommend this course?: Yes

Xixi Wang

4.5
21/12/2016
What I would highlight: Great hands-on lab to kick you off rapidly. Be that as it may, the instructional class is not all that identified with the lab. Better bring it with a book on Spark.
What could be improved: No negative aspects.
Would you recommend this course?: Yes
*All reviews collected by Emagister & iAgora have been verified

This centre's achievements

2017

All courses are up to date

The average rating is higher than 3.7

More than 50 reviews in the last 12 months

This centre has featured on Emagister for 8 years

Subjects

  • Data analysis
  • Spark
  • Big Data
  • Science Techniques
  • Programming

Course programme

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 Spark, is required.

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Big Data Analysis with Spark - University of California

Free

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