R Yazılımı ile Veri Görselleştirme ve Principal Component Analysis (PCA) Eğitim Programı 2016 - 2017 Eğitim Döneminde başlıyor.

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Değişen konjonktürde artan ve çeşitlenen riskler yeni Risk Yönetimi ve tespit araçlarını zorluyor. Özellikle Bankacılık sektöründe riskin erken teşhisinde #R Software ve güçlü analitik paketleri ile yapılan analizler artık en çok tercih edilenler arasında. Karar destek sistemlerinizde R ile çok boyutlu Risk profili analizi yapmak için; Bankacılık dünyasına özel; R Yazılımı ile yeni Risk Analizi Eğitim Modülü Principal Componenet Analysis (PCA) Eğitim Modülü Eylülde ajandanıza kaydedin Bilgi için; fatma.cinar@datalabtr.com; info@datalabtr.comwww.datalabtr.com; Derslerin Adları ve İçerikleri; R ve Paketleri R ile Data Okuma ve Kaydetme İstatistik Data Modelleme Linear-NonLinear Regresyon ve Anova Genel Linear Modeller (GLM) Lojistik Regresyon Multivariate Analysis Principal Component Analysis (PCA) Factor Analysis (FA) Correspondence Analysis (CA) Kümeleme (Cluster) Analizi (k-means) Bütün bu konular R RcmdrGui ve FactoMineR FactorExtra Paketleri ile işlenecek ve R Grafik Paketleri ile de desteklenecektir. Genel Değerlendirme ve Kapanış

Dados e análise

01
Veri ve
Uzaklıklar
Veri hazırlama
Gerekli R
Paketleri
Uzaklık
metrikleri
02
Temel
Kümeleme
Optimal Küme
Sayısı
Basit
Kümeleme
Hiyerarşik
Kümeleme
03
Kümelenmenin
Gerçeklenmesi
Kümeleme
Eğilimler
Gerçekleme
İstatistikleri
Hiyerarşik
Kümeleme için
P değeri
04 05
Küme
Görselleştirme
Kümelemeyi
Görsel
Güçlendirme
Güzel Ağaç
Diyagramları
Statik ve
etkileşimli
Heatmap
Gelişmiş
Kümeleme
Fuzy
Kümeleme
Model
Kümeleme
Density-
Based
Kümeleme
Hybrid
yöntemler
UNSUPERVISED MACHINE LEARNING TEKNİKLERİ
Kümeleme, Boyut İndirgeme, Etkin Görselleştirme

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