From Data to Decision with Big Data and Predictive Analytics Treningskurs

Kurskode

d2dbdpa

Varighet

21 timer (vanligvis 3 dag inkludert pauser)

Krav

Understanding of traditional data management and analysis methods like SQL, data warehouses, business intelligence, OLAP, etc... Understanding of basic statistics and probability (mean, variance, probability, conditional probability, etc....)

Oversikt

Publikum

Hvis du prøver å være fornuftig ut fra dataene du har tilgang til eller ønsker å analysere ustrukturerte data tilgjengelig på nettet (som Twitter, koblet inn osv.), Er dette kurset noe for deg.

Det er mest rettet mot beslutningstakere og personer som trenger å velge hvilke data som er verdt å samle inn og hva som er verdt å analysere.

Det er ikke rettet mot folk som konfigurerer løsningen, de menneskene vil dra nytte av det store bildet.

Leveringsmodus

I løpet av kurset vil delegatene bli presentert med fungerende eksempler på stort sett åpen kildekode-teknologier.

Korte forelesninger blir fulgt av presentasjon og enkle øvelser av deltakerne

Innhold og programvare brukt

All programvare som brukes oppdateres hver gang kurset kjøres, så vi sjekker de nyeste versjonene som er mulig.

Den dekker prosessen fra innhenting, formatering, behandling og analyse av dataene, for å forklare hvordan man kan automatisere beslutningsprosesser med maskinlæring.

Machine Translated

Kursplan

Quick Overview

  • Data Sources
  • Minding Data
  • Recommender systems
  • Target Marketing

Datatypes

  • Structured vs unstructured
  • Static vs streamed
  • Attitudinal, behavioural and demographic data
  • Data-driven vs user-driven analytics
  • data validity
  • Volume, velocity and variety of data

Models

  • Building models
  • Statistical Models
  • Machine learning

Data Classification

  • Clustering
  • kGroups, k-means, the nearest neighbours
  • Ant colonies, birds flocking

Predictive Models

  • Decision trees
  • Support vector machine
  • Naive Bayes classification
  • Neural networks
  • Markov Model
  • Regression
  • Ensemble methods

ROI

  • Benefit/Cost ratio
  • Cost of software
  • Cost of development
  • Potential benefits

Building Models

  • Data Preparation (MapReduce)
  • Data cleansing
  • Choosing methods
  • Developing model
  • Testing Model
  • Model evaluation
  • Model deployment and integration

Overview of Open Source and commercial software

  • Selection of R-project package
  • Python libraries
  • Hadoop and Mahout
  • Selected Apache projects related to Big Data and Analytics
  • Selected commercial solution
  • Integration with existing software and data sources

Testimonials

★★★★★
★★★★★

Related Categories

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