{"id":5944,"date":"2021-05-24T10:00:22","date_gmt":"2021-05-24T07:00:22","guid":{"rendered":"https:\/\/www.casper.com.tr\/excaliburclan\/?p=5944"},"modified":"2024-06-11T16:30:59","modified_gmt":"2024-06-11T13:30:59","slug":"veri-bilimi-10-temel-beceri","status":"publish","type":"post","link":"https:\/\/www.casper.com.tr\/excaliburclan\/veri-bilimi-10-temel-beceri\/","title":{"rendered":"Veri Bilimi: 10 Temel Beceri"},"content":{"rendered":"\r\n

Bu aralar, sizin de kula\u011f\u0131n\u0131za \u00e7ok \u00e7al\u0131nm\u0131\u015ft\u0131r: \u201cGelece\u011fin en \u00f6nemli meslekleri aras\u0131nda veri bilimi \u00f6nemli yer tutacak.\u201d Biz de Clan \u00fcyelerimizi gelece\u011fe haz\u0131rlamak i\u00e7in, faydal\u0131 bir derlemeye yer vermek istedik. Veri Bilimi: 10 Temel Beceri yaz\u0131m\u0131z\u0131, bu alanda ara\u015ft\u0131rma yapmak ve yeni yetenekler edinmek isteyenler i\u00e7in, yol g\u00f6sterici olmas\u0131 amac\u0131yla ekledik. Bu yaz\u0131n\u0131n olu\u015fmas\u0131 i\u00e7in bize kaynak olan \u015fu makaleye g\u00f6z atman\u0131z\u0131<\/a> \u00f6neririz.<\/p>\r\n

 <\/p>\r\n

Veri Bilimi, veri haz\u0131rlama ve ara\u015ft\u0131rma gibi \u00e7e\u015fitli alt b\u00f6l\u00fcmleri i\u00e7eren \u00e7ok geni\u015f bir aland\u0131r. Bu alanda veri g\u00f6sterimi ve d\u00f6n\u00fc\u015f\u00fcm\u00fc; veri g\u00f6rselle\u015ftirme ve sunumu; tahmine dayal\u0131 analitik; makine \u00f6\u011frenimi vb. ba\u015fl\u0131klar i\u00e7erir. Yeni ba\u015flayanlar i\u00e7in \u015fu soruyu sormak do\u011fald\u0131r: \u201cVeri bilimcisi olmak i\u00e7in hangi bilgi ve becerilere ihtiyac\u0131m var?\u201d<\/p>\r\n

 <\/p>\r\n

S\u0131ralayaca\u011f\u0131m\u0131z beceriler 2 k\u00fcmede yer al\u0131yor. Birincisi teknolojik beceriler (Matematik ve \u0130statistik, Kodlama, Veri \u0130\u015fleme ve \u00d6n \u0130\u015fleme, Veri G\u00f6rselle\u015ftirme, Makine \u00d6\u011frenmesi ve Ger\u00e7ek D\u00fcnya Proje Becerileri). \u0130kincisi k\u00fcmedeki sosyal beceriler ise \u201c\u0130leti\u015fim Becerileri\u201d ve \u201cYa\u015fam Boyu \u00d6\u011frenme\u201d olmak \u00fczere 2 kategoriye ayr\u0131labilir. Tak\u0131m oyuncusu olma ve etik de yine bu k\u00fcmenin i\u00e7inde yer alan kavramlar. Laf\u0131 uzatmayal\u0131m ve listeyi olu\u015fturmaya ba\u015flayal\u0131m.<\/p>\r\n

 <\/p>\r\n

1.Matematik ve \u0130statistik<\/h5>\r\n

(I) \u0130statistik ve Olas\u0131l\u0131k<\/strong><\/p>\r\n

\u0130statistikler ve Olas\u0131l\u0131k, \u00f6zelliklerin g\u00f6rselle\u015ftirilmesi, veri \u00f6n i\u015fleme, \u00f6zellik d\u00f6n\u00fc\u015f\u00fcm\u00fc, veri atama, boyut azaltma, \u00f6zellik m\u00fchendisli\u011fi, model de\u011ferlendirme vb. Gereksinimler i\u00e7in kullan\u0131l\u0131r. A\u015fina olman\u0131z gereken konular \u015funlard\u0131r:<\/p>\r\n

 <\/p>\r\n

a) Ortalama<\/p>\r\n

b) Medyan<\/p>\r\n

c) Mod<\/p>\r\n

d) Standart Sapma \/ Varyans<\/p>\r\n

e) Korelasyon Katsay\u0131s\u0131 ve Kovaryans matrisi<\/p>\r\n

f) Olas\u0131l\u0131k Da\u011f\u0131l\u0131mlar\u0131 (Binom, Poisson, Normal)<\/p>\r\n

g) p-de\u011feri<\/p>\r\n

h) MSE (Ortalama Kare Hatas\u0131)<\/p>\r\n

i) R2 Puan\u0131<\/p>\r\n

j) Baye Teoremi (\u00d6rne\u011fin: Kesinlik, Geri \u00c7a\u011f\u0131rma, Pozitif \u00d6ng\u00f6r\u00fcc\u00fc De\u011fer, Negatif \u00d6ng\u00f6r\u00fcc\u00fc De\u011fer, Kar\u0131\u015f\u0131kl\u0131k Matrisi, ROC E\u011frisi)<\/p>\r\n

k) A \/ B Testi<\/p>\r\n

l) Monte Carlo Sim\u00fclasyonu<\/p>\r\n

 <\/p>\r\n

(II) \u00c7ok De\u011fi\u015fkenli Analiz<\/strong><\/p>\r\n

 <\/p>\r\n

\u00c7o\u011fu makine \u00f6\u011frenmesi modeli, birka\u00e7 \u00f6zelli\u011fe veya \u00f6ng\u00f6r\u00fcc\u00fcye sahip bir veri k\u00fcmesiyle olu\u015fturulur. Bu nedenle, \u00e7ok de\u011fi\u015fkenli analize a\u015final\u0131k, bir makine \u00f6\u011frenmesi modeli olu\u015fturmak i\u00e7in son derece \u00f6nemlidir. A\u015fina olman\u0131z gereken konular \u015funlard\u0131r:<\/p>\r\n

 <\/p>\r\n

a) Birka\u00e7 de\u011fi\u015fkenli fonksiyonlar<\/p>\r\n

b) T\u00fcrevler ve gradyanlar<\/p>\r\n

c) Ad\u0131m fonksiyonu, Sigmoid fonksiyonu, Logit fonksiyonu, ReLU (Rectified Linear Unit) fonksiyonu<\/p>\r\n

d) Maliyet fonksiyonu<\/p>\r\n

e) Fonksiyonlar\u0131n grafiklendirilmesi<\/p>\r\n

f) Bir fonksiyonun asgari ve azami de\u011ferleri<\/p>\r\n

 <\/p>\r\n

(III) Do\u011frusal Cebir<\/strong><\/p>\r\n

Do\u011frusal cebir, makine \u00f6\u011frenimindeki en \u00f6nemli matematik becerisidir. Bir veri seti, bir matris olarak temsil edilir. Veri \u00f6n i\u015fleme, veri d\u00f6n\u00fc\u015ft\u00fcrme ve model de\u011ferlendirmede kullan\u0131l\u0131r. Bunun sonucunda a\u015fina olman\u0131z gereken konular \u015funlard\u0131r:<\/p>\r\n

 <\/p>\r\n

a) Vekt\u00f6rler<\/p>\r\n

b) Matrisler<\/p>\r\n

c) Bir matrisin transpoze edilmesi<\/p>\r\n

d) Bir matrisin tersi<\/p>\r\n

e) Bir matrisin determinant\u0131<\/p>\r\n

f) Nokta \u00e7arp\u0131m<\/p>\r\n

g) \u00d6zde\u011ferler<\/p>\r\n

h) \u00d6zvekt\u00f6rler<\/p>\r\n

 <\/p>\r\n

(IV) Optimizasyon Y\u00f6ntemleri<\/strong><\/p>\r\n

 <\/p>\r\n

\u00c7o\u011fu makine \u00f6\u011frenmesi algoritmas\u0131, tahmini modelleme ger\u00e7ekle\u015ftirir. Bunu Nesnel bir i\u015flevi en aza indirerek yapar. <\/span>Bu nedenle tahmin edilen etiketleri elde etmek i\u00e7in test verilerine uygulanmas\u0131 gereken a\u011f\u0131rl\u0131klar\u0131 da \u00f6\u011frenir<\/span><\/span>.<\/span><\/p>\r\n

Bu y\u00fczden a\u015fina olman\u0131z gereken konular \u015funlard\u0131r:<\/p>\r\n

 <\/p>\r\n

a) Maliyet i\u015flevi \/ Ama\u00e7 i\u015flevi<\/p>\r\n

b) Olabilirlik i\u015flevi<\/p>\r\n

c) Hata i\u015flevi<\/p>\r\n

d) Gradyan \u0130ni\u015f Algoritmas\u0131 ve \u00c7e\u015fitleri (\u00d6rne\u011fin Stokastik Gradyan \u0130ni\u015f Algoritmas\u0131)<\/p>\r\n

\"Temel
Temel Programlama Becerileri<\/figcaption><\/figure>\r\n

 <\/p>\r\n

2. Temel Programlama Becerileri<\/strong><\/h5>\r\n

 <\/p>\r\n

Veri biliminde programlama becerileri \u00e7ok \u00f6nemlidir. \u00d6rne\u011fin Python ve R, veri biliminde en pop\u00fcler 2 programlama dili olarak kabul edilir. \u00d6zellikle her iki dilde de temel bilgi sahibi olman\u0131z \u00e7ok \u00f6nemlidir. Baz\u0131 kurulu\u015flarda kariyer yapmak i\u00e7in yaln\u0131zca R veya Python bilmeniz gerektirebilir. Ancak baz\u0131lar\u0131nda ise ikisini birden bilmelisiniz.<\/p>\r\n

 <\/p>\r\n

(I) Python<\/strong><\/p>\r\n

Python’daki temel programlama becerilerine a\u015fina olun. \u0130\u015fte nas\u0131l kullan\u0131laca\u011f\u0131na hakim olman\u0131z gereken en \u00f6nemli paketler:<\/p>\r\n

 <\/p>\r\n

a) Numpy<\/p>\r\n

b) Pandas<\/p>\r\n

c) Matplotlib<\/p>\r\n

d) Seaborn<\/p>\r\n

e) Scikit-learn<\/p>\r\n

f) PyTorch<\/p>\r\n

 <\/p>\r\n

(ii) Skills in R<\/strong><\/p>\r\n

a) Tidyverse<\/p>\r\n

b) Dplyr<\/p>\r\n

c) Ggplot2<\/p>\r\n

d) Caret<\/p>\r\n

e) Stringr<\/p>\r\n

 <\/p>\r\n

(iii) Di\u011fer Programlama Dillerinde Beceriler<\/strong><\/p>\r\n

A\u015fa\u011f\u0131daki end\u00fcstri standartlar\u0131 veya programlama dilleri, baz\u0131 kurulu\u015flar veya end\u00fcstriler taraf\u0131ndan elzem olabilir:<\/p>\r\n

a) Excel<\/p>\r\n

b) Tableau<\/p>\r\n

c) Hadoop<\/p>\r\n

d) SQL<\/p>\r\n

e) Spark<\/p>\r\n

\"Veri
Veri D\u00fczeltme ve \u00d6n \u0130\u015fleme Becerileri<\/figcaption><\/figure>\r\n

 <\/p>\r\n

3. Veri D\u00fczeltme ve \u00d6n \u0130\u015fleme Becerileri<\/h5>\r\n

Veriler, ister \u00e7\u0131kar\u0131ma dayal\u0131 analiz, \u00f6ng\u00f6r\u00fcc\u00fc analiz veya kuralc\u0131 analiz tipinde olsun. Veri biliminde, herhangi t\u00fcrdeki bir analiz i\u00e7in yap\u0131 ta\u015f\u0131d\u0131r. Bir modelin tahmin g\u00fcc\u00fc, modelin olu\u015fturulmas\u0131nda kullan\u0131lan verilerin kalitesine ba\u011fl\u0131d\u0131r. Veriler, metin, tablo, g\u00f6r\u00fcnt\u00fc, ses veya video gibi farkl\u0131 bi\u00e7imlerde gelir. \u00c7o\u011fu zaman, analiz i\u00e7in kullan\u0131lan verilerin daha fazla analiz i\u00e7in uygun bir bi\u00e7ime d\u00f6n\u00fc\u015ft\u00fcr\u00fclmesi gerekir. Bunu madencilik olarak tabir edilen kavrama uygun olarak “\u00e7\u0131kar\u0131lmas\u0131”, i\u015flenmesi ve d\u00f6n\u00fc\u015ft\u00fcr\u00fclmesi olarak tan\u0131mlar\u0131z.<\/p>\r\n

 <\/p>\r\n

i) Veri \u0130\u015fleme: <\/strong>Veri i\u015fleme s\u00fcreci, herhangi bir veri bilimcisi i\u00e7in kritik bir ad\u0131md\u0131r. \u00c7ok nadiren, analiz i\u00e7in bir veri bilimi projesinde verilere kolayca eri\u015filebilir. Verilerin bir dosyada, veritaban\u0131nda sakl\u0131 olmas\u0131 olas\u0131d\u0131r. Ancak web sayfalar\u0131, tweet’ler veya PDF’ler gibi belgelerden taranarak \u00e7\u0131kar\u0131lmas\u0131 da olas\u0131d\u0131r. Verileri nas\u0131l kar\u0131\u015ft\u0131raca\u011f\u0131n\u0131z\u0131 ve temizleyece\u011finizi bilmek olduk\u00e7a \u00f6nemli. Aksi takdirde habersiz kalaca\u011f\u0131n\u0131z kritik i\u00e7g\u00f6r\u00fcler elde etmenizi sa\u011flayacakt\u0131r.<\/p>\r\n

 <\/p>\r\n

ii) Veri \u00d6n \u0130\u015fleme:<\/strong> Veri \u00f6n i\u015fleme hakk\u0131nda kavramsal bilginin \u00f6tesinde yetkinli\u011fe sahip olmak \u00e7ok \u00f6nemlidir. Ve sonu\u00e7 olarak a\u015fa\u011f\u0131daki gibi konular\u0131 i\u00e7erir:<\/p>\r\n

a) Eksik verilerle ba\u015fa \u00e7\u0131kmak<\/p>\r\n

b) Veri isnat etme<\/p>\r\n

c) Kategorik verileri i\u015fleme<\/p>\r\n

d) S\u0131n\u0131fland\u0131rma problemleri i\u00e7in kodlama s\u0131n\u0131f etiketleri<\/p>\r\n

e) Temel Bile\u015fen Analizi (PCA) ve Do\u011frusal Ayr\u0131m Analizi (LDA) gibi \u00f6zellik d\u00f6n\u00fc\u015ft\u00fcrme ve boyut azaltma teknikleri.<\/p>\r\n

\"Veri
Veri G\u00f6rselle\u015ftirme Becerileri<\/figcaption><\/figure>\r\n

 <\/p>\r\n

4. Veri G\u00f6rselle\u015ftirme Becerileri<\/strong><\/h5>\r\n

 <\/p>\r\n

\u0130yi bir veri g\u00f6rselle\u015ftirmesinin temel bile\u015fenlerine hakim olman\u0131z gerekir.<\/p>\r\n

 <\/p>\r\n

a) Veri Bile\u015feni:<\/strong> Verilerin nas\u0131l g\u00f6rselle\u015ftirilece\u011fine karar vermenin \u00f6nemli bir ilk ad\u0131m\u0131, verilerin ne t\u00fcr veri oldu\u011funu bilmektir. \u00d6rne\u011fin: Kategorik veriler, Ayr\u0131k veriler, S\u00fcrekli veriler, Zaman Serisi verileri vb.<\/p>\r\n

b) Geometrik Bile\u015fen:<\/strong> Verileriniz i\u00e7in ne t\u00fcr g\u00f6rselle\u015ftirmenin uygun oldu\u011funa bu a\u015famada karar verirsiniz. \u00d6rne\u011fin: Da\u011f\u0131l\u0131m Grafi\u011fi, \u00c7izgi Grafikleri, Barplotlar, Histogramlar, Qqplots, Yo\u011funluklar, Is\u0131 haritalar\u0131 vb.<\/p>\r\n

c) E\u015fleme Bile\u015feni: <\/strong>Burada, X’e hangi de\u011fi\u015fkeni, Y’ye hangi de\u011fi\u015fkeni tan\u0131mlayaca\u011f\u0131n\u0131za karar vermelisiniz. Bu, \u00f6zellikle veri k\u00fcmeniz \u00e7e\u015fitli \u00f6zelliklerle \u00e7ok boyutlu oldu\u011funda \u00f6nemlidir.<\/p>\r\n

d) \u00d6l\u00e7ek Bile\u015feni: <\/strong>Burada ne t\u00fcr \u00f6l\u00e7eklerin kullan\u0131laca\u011f\u0131na karar verirsiniz. \u00d6r: Do\u011frusal \u00d6l\u00e7ek, G\u00fcnl\u00fck \u00d6l\u00e7ek vb.<\/p>\r\n

e) Etiketler Bile\u015feni: <\/strong>Bu, eksen etiketleri, ba\u015fl\u0131klar, g\u00f6stergeler, kullan\u0131lacak yaz\u0131 tipi boyutu vb. tamamen g\u00f6rsel kriterleri i\u00e7erir.<\/p>\r\n

f) Etik Bile\u015fen:<\/strong> Burada, g\u00f6rselle\u015ftirmenizin ger\u00e7ek hikayeyi anlatt\u0131\u011f\u0131ndan emin olmak istersiniz. Bir veri g\u00f6rselle\u015ftirmesini temizlerken, \u00f6zetlerken, i\u015flerken veya \u00fcretirken eylemlerinizin fark\u0131nda olman\u0131z gerekir. G\u00f6rselle\u015ftirmenizi hedef kitlenizi yan\u0131ltmak veya manip\u00fcle etmek i\u00e7in kullanmad\u0131\u011f\u0131n\u0131zdan emin olman\u0131z gerekir.<\/p>\r\n

\"Temel
Temel Makine \u00d6\u011frenmesi Becerileri<\/figcaption><\/figure>\r\n
5. Temel Makine \u00d6\u011frenmesi Becerileri<\/strong><\/h5>\r\n

Makine \u00d6\u011frenmesi, veri biliminin \u00e7ok \u00f6nemli bir dal\u0131d\u0131r. Makine \u00f6\u011frenmesi \u00e7er\u00e7evesini \u00e7ok iyi anlamak hayati \u00f6neme sahiptir. Bu sebeple Problem \u00c7er\u00e7eveleme; Veri Analizi; Model Olu\u015fturma, Test Etme ve De\u011ferlendirme ve Model Uygulamas\u0131 yer al\u0131r.<\/p>\r\n

A\u015fa\u011f\u0131dakiler a\u015fina olunmas\u0131 gereken \u00f6nemli makine \u00f6\u011frenimi algoritmalar\u0131d\u0131r.<\/p>\r\n

i) Denetimli \u00d6\u011frenme (S\u00fcrekli De\u011fi\u015fken Tahmin)<\/p>\r\n

a) Temel Regresyon<\/p>\r\n

b) \u00c7oklu Regresyon analizi<\/p>\r\n

c) D\u00fczenli Regresyon<\/p>\r\n

ii) Denetimli \u00d6\u011frenme (Kesikli De\u011fi\u015fken Tahmin)<\/p>\r\n

a) Lojistik Regresyon S\u0131n\u0131fland\u0131r\u0131c\u0131<\/p>\r\n

b) Destek Vekt\u00f6r Makinesi S\u0131n\u0131fland\u0131r\u0131c\u0131s\u0131<\/p>\r\n

c) K-en Yak\u0131n Kom\u015fu (KNN) S\u0131n\u0131fland\u0131r\u0131c\u0131s\u0131<\/p>\r\n

d) Karar A\u011fac\u0131 S\u0131n\u0131fland\u0131r\u0131c\u0131s\u0131<\/p>\r\n

e) Rastgele Orman S\u0131n\u0131fland\u0131r\u0131c\u0131s\u0131<\/p>\r\n

iii) Denetimsiz \u00d6\u011frenme<\/p>\r\n

a) K-Means Algoritmas\u0131<\/p>\r\n

 <\/p>\r\n

6. Ger\u00e7ek D\u00fcnya Capstone (Bitirme) Projeleri<\/strong><\/h5>\r\n

Kurs ya da labaratuvar \u00e7al\u0131\u015fmas\u0131ndan kazan\u0131lan beceriler tek ba\u015f\u0131na sizi bir veri bilimcisi yapmaz. Nitelikli bir veri bilimcisi, problem \u00e7er\u00e7eveleme, veri toplama ve analizi, model olu\u015fturma, model testi, model de\u011ferlendirme gibi veri bilimi ve makine \u00f6\u011frenmesi s\u00fcrecindeki her a\u015famay\u0131 i\u00e7eren ger\u00e7ek d\u00fcnya veri bilimi projesinin ba\u015far\u0131l\u0131 bir \u015fekilde tamamland\u0131\u011f\u0131na dair kan\u0131t g\u00f6sterebilmelidir. Bu sebeple ger\u00e7ek d\u00fcnya veri bilimi projeleri a\u015fa\u011f\u0131daki \u015fekillerde bulunabilir:<\/p>\r\n

 <\/p>\r\n

a) Kaggle Projeleri<\/p>\r\n

b) Stajlar<\/p>\r\n

c) G\u00f6r\u00fc\u015fmeler<\/p>\r\n

 <\/p>\r\n

7. \u0130leti\u015fim Becerileri<\/h5>\r\n

Veri bilimcileri; fikirlerini ekip \u00fcyelerine veya i\u015fletme y\u00f6neticilerine net olarak iletebilmelidir. \u0130leti\u015fim becerilerinin geli\u015fmi\u015f olmas\u0131 \u015funun i\u00e7in de \u00f6nemlidir. <\/span>\u00d6rne\u011fin: teknik kavramlar\u0131 \u00e7ok az anlayan veya hi\u00e7 anlayamayan insanlara aktarabilmek i\u00e7in<\/span><\/span>. \u0130yi ileti\u015fim becerileri, veri analistleri, veri m\u00fchendisleri, saha m\u00fchendisleri vb. gibi di\u011fer ekip \u00fcyeleriyle birlik ve beraberli\u011fi geli\u015ftirmeye yard\u0131mc\u0131 olacakt\u0131r. Dolay\u0131s\u0131yla ileti\u015fim becerileri veri biliminde \u00f6nemli rol oynar.<\/span><\/p>\r\n

 <\/p>\r\n

8. \u00d6\u011frenmeyi Hayat Boyu S\u00fcrd\u00fcrme<\/strong><\/h5>\r\n

Veri bilimi s\u00fcrekli geli\u015fen bir aland\u0131r. Bu nedenle yeni teknolojileri kucaklamaya ve \u00f6\u011frenmeye haz\u0131r olun. Alandaki geli\u015fmelerden haberdar olman\u0131n bir yolu, di\u011fer veri bilimcileriyle a\u011f kurmakt\u0131r. A\u011f olu\u015fturmay\u0131 destekleyen baz\u0131 platformlar LinkedIn, Reddit, Github ve Medium’dur. (T\u00fcrkiye’de Ek\u015fiS\u00f6zl\u00fck’te, bu alanda yaz\u0131 payla\u015fan yazarlarla da ileti\u015fime ge\u00e7ebilirsiniz.) Platformlar, alandaki son geli\u015fmeler hakk\u0131nda g\u00fcncel bilgileri edinmek i\u00e7in \u00e7ok kullan\u0131\u015fl\u0131d\u0131r.<\/p>\r\n

 <\/p>\r\n

9. Tak\u0131m Oyuncu Becerileri<\/strong><\/h5>\r\n

Bir veri bilimcisi olarak, veri analistleri, m\u00fchendisler, y\u00f6neticilerden olu\u015fan bir ekipte \u00e7al\u0131\u015facaks\u0131n\u0131z. Bu nedenle iyi ileti\u015fim becerilerine ihtiyac\u0131n\u0131z var. \u0130yi bir dinleyici olmal\u0131s\u0131n\u0131z. <\/span>Harika bir veri bilimi projesi tasarlayabilmek i\u00e7in m\u00fchendislere veya di\u011fer \u00e7al\u0131\u015fanlara, proje geli\u015ftirmenin erken a\u015famalar\u0131nda g\u00fcvenmelisiniz<\/span><\/span>. Ba\u015fka bir deyi\u015fle iyi bir tak\u0131m oyuncusu olmak olduk\u00e7a \u00f6nemli. Bu i\u015f ortam\u0131nda ba\u015far\u0131l\u0131 olman\u0131za ve ekibinizin di\u011fer \u00fcyeleri ve y\u00f6neticilerinizle iyi ili\u015fkiler s\u00fcrd\u00fcrmenize yard\u0131mc\u0131 olur. Bu da \u00f6zetle sizi daima ileriye ta\u015f\u0131r.<\/span><\/p>\r\n

\u00a0<\/h5>\r\n
10. Veri Biliminde Etik<\/strong><\/h5>\r\n

Projenizin ger\u00e7ek ve derin anlam\u0131n\u0131 haiz olun. Kendinize kar\u015f\u0131 d\u00fcr\u00fcst olun. Verileri manip\u00fcle etmekten veya sonu\u00e7larda kas\u0131tl\u0131 olarak \u00f6nyarg\u0131 olu\u015fturacak bir y\u00f6ntem kullanmaktan ka\u00e7\u0131n\u0131n. Veri toplamadan analize, model olu\u015fturmaktan test etmeye ve tabii ki uygulamaya kadar t\u00fcm a\u015famalarda etik olun. Hedef kitlenizi yan\u0131ltmak veya manip\u00fcle etmek amac\u0131yla sonu\u00e7lar uydurmaktan ka\u00e7\u0131n\u0131n. Veri bilimi projenizden elde etti\u011finiz bulgular\u0131 yorumlama \u015feklinizde etik olun. Sonu\u00e7 olarak en \u00f6nemli kriterlerden birinin etik oldu\u011funu unutmay\u0131n.<\/p>\r\n

 <\/p>\r\n

Veri bilimi s\u00fcrekli geli\u015fen bir aland\u0131r, ancak veri biliminin temellerine hakim olmak size derin \u00f6\u011frenme, yapay zeka vb. gibi geli\u015fmi\u015f kavramlar\u0131 takip etmek i\u00e7in ihtiya\u00e7 duydu\u011funuz gerekli arka plan\u0131 sa\u011flayacakt\u0131r. Veri Bilimi: 10 Temel Beceri makalemiz, bu alanda ara\u015ft\u0131rma yapmak ve yeni yetenekler edinmek isteyenler i\u00e7in do\u011fru bir ba\u015flang\u0131\u00e7 noktas\u0131 olacak. Daha fazla i\u00e7erik i\u00e7in g\u00f6z atmay\u0131 unutmay\u0131n: Excalibur Clan<\/a><\/p>\r\n","protected":false},"excerpt":{"rendered":"

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