Finding Insights and Making Predictions - Statistics as your Crystal Ball

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Wichtige informationen

Tipologie Kurs
Beginn Oktober
  • Kurs
  • Beginn:
    Oktober
Beschreibung

This intermediate course is for data analysts confronted with big data problems.

Wichtige informationen
Veranstaltungsort(e)

Wo und wann

Beginn Lage
Oktober
Beginn Oktober
Lage

Häufig gestellte Fragen

· Voraussetzungen

You should have:

  • Experience with IBM SPSS syntax
  • Advanced knowledge on statistical analysis

Was lernen Sie in diesem Kurs?

  • IT solutions to data sets with high memory demand
  • Automated data processing
  • Data mining and exploration
  • Techniques for automated model selection

Themenkreis

This course is developed and owned by an IBM Authorized Global Training Provider: Arrow ECS

Derivative Works Courses are developed by an IBM authorized Global Training Provider. The content of the course is based on IBM products and services. It offers skills or services that IBM does not offer in the course catalog today and are intended to compliment our existing portfolio. The course is developed by the Training Provider. IBM solely provides a market place to advertise the courses on our external websites, ibm.com/training and lists the Global Training Provider who owns the content.

This course is part 4 of 7 of the IBM Statistics Academy powered by Arrow Education.

Topics very typical when dealing with Big Data will be the main subject of this 2-day module of the Statistics Academy - word, vision and most important trend in data analysis of the past years. Big data combines various concepts and techniques to analyze large data sets. This training enables participants to master big data sets. We will show how to adequately address the challenge of big data sets and give an insight on applicable data mining techniques. Furthermore, we show how to incorporate concepts that will enable participants to present their findings.

Erfolge des Zentrums

Zusätzliche Informationen

The course will enable participants to handle large datasets.


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