Quality Seal Emagister EMAGISTER CUM LAUDE

Big Data Analysis with Spark - University of California

edX
Online
4 opinioni

Gratis

Informazione importanti

  • Corso
  • Online
  • Durata:
    4 Weeks
  • Quando:
    Flessible
Descrizione

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.

Informazione importanti

Requisiti: 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.

Sedi

Dove e quando

Inizio Luogo
Flessible
Online

Opinioni

X

14/09/2016
Il meglio good hands-on lab to get you started quickly. But the lecture is not so related to the lab. Better take it with a book on Spark.

Da migliorare No negative aspects.

Corso realizzato: Settembre 2016 | Recomendarías este centro? Sí.
E

07/10/2016
Il meglio Great course organization, especially the balance between theory and practice. Some tasks were too easy and some were not clear at first, but piazza search usually helped. I consider this is a very good pyspark tutorial with explanation of spark key features.

Da migliorare N/A.

Corso realizzato: Ottobre 2016 | Recomendarías este centro? Sí.
E

09/11/2015
Il meglio A lot of overlapping with the 2 other courses of the xSerie. I would definitely not advise taking this course if you took them. The last of the 4 weeks consists of only 20 minutes of video explaining very basic statistic concepts.

Da migliorare Nothing.

Corso realizzato: Novembre 2015 | Recomendarías este centro? Sí.

Cosa impari in questo corso?

Data analysis
Programming
Big Data
Spark
Science Techniques

Programma

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.