A Practical Introduction to Data Analysis and Big Data Training Course

Kurskode

bigdata_

Varighet

35 timer (usually 5 days including breaks)

Krav

  • A general understanding of math.
  • A general understanding of programming.
  • A general understanding of databases.

Audience

  • Developers / programmers
  • IT consultants

Oversikt

Deltakere som fullfører denne instruktørledede, liveopplæringen, vil få en praktisk, virkelighetsnær forståelse av Big Data og tilhørende teknologier, metoder og verktøy.

Deltakerne vil ha muligheten til å ta denne kunnskapen ut i livet gjennom praktiske øvelser. Gruppeinteraksjon og tilbakemelding fra instruktører utgjør en viktig del av klassen.

Kurset starter med en introduksjon til elementære konsepter av Big Data , og går deretter over i programmeringsspråk og metodikk som brukes for å utføre Data Analysis . Til slutt diskuterer vi de verktøy og infrastruktur som gjør at Big Data lagring, distribuert prosessering, og Scala heten.

Kursets format

  • Delforedrag, deldiskusjon, praktisk praksis og implementering, sporadisk spørring for å måle fremgang.

Machine Translated

Kursplan

Introduction to Data Analysis and Big Data

  • What Makes Big Data "Big"?
    • Velocity, Volume, Variety, Veracity (VVVV)
  • Limits to Traditional Data Processing
  • Distributed Processing
  • Statistical Analysis
  • Types of Machine Learning Analysis
  • Data Visualization

Big Data Roles and Responsibilities

  • Administrators
  • Developers
  • Data Analysts

Languages Used for Data Analysis

  • R Language
    • Why R for Data Analysis?
    • Data manipulation, calculation and graphical display
  • Python
    • Why Python for Data Analysis?
    • Manipulating, processing, cleaning, and crunching data

Approaches to Data Analysis

  • Statistical Analysis
    • Time Series analysis
    • Forecasting with Correlation and Regression models
    • Inferential Statistics (estimating)
    • Descriptive Statistics in Big Data sets (e.g. calculating mean)
  • Machine Learning
    • Supervised vs unsupervised learning
    • Classification and clustering
    • Estimating cost of specific methods
    • Filtering
  • Natural Language Processing
    • Processing text
    • Understaing meaning of the text
    • Automatic text generation
    • Sentiment analysis / topic analysis
  • Computer Vision
    • Acquiring, processing, analyzing, and understanding images
    • Reconstructing, interpreting and understanding 3D scenes
    • Using image data to make decisions

Big Data Infrastructure

  • Data Storage
    • Relational databases (SQL)
      • MySQL
      • Postgres
      • Oracle
    • Non-relational databases (NoSQL)
      • Cassandra
      • MongoDB
      • Neo4js
    • Understanding the nuances
      • Hierarchical databases
      • Object-oriented databases
      • Document-oriented databases
      • Graph-oriented databases
      • Other
  • Distributed Processing
    • Hadoop
      • HDFS as a distributed filesystem
      • MapReduce for distributed processing
    • Spark
      • All-in-one in-memory cluster computing framework for large-scale data processing
      • Structured streaming
      • Spark SQL
      • Machine Learning libraries: MLlib
      • Graph processing with GraphX
  • Scalability
    • Public cloud
      • AWS, Google, Aliyun, etc.
    • Private cloud
      • OpenStack, Cloud Foundry, etc.
    • Auto-scalability

Choosing the Right Solution for the Problem

The Future of Big Data

Summary and Conclusion

Testimonials

★★★★★
★★★★★

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