{"id":56383,"date":"2024-06-29T16:00:57","date_gmt":"2024-06-29T13:00:57","guid":{"rendered":"https:\/\/www.englishcentral.com\/blog\/?p=56383"},"modified":"2024-07-17T10:29:39","modified_gmt":"2024-07-17T07:29:39","slug":"veri-bilimi-icin-is-ingilizcesi","status":"publish","type":"post","link":"https:\/\/www.englishcentral.com\/blog\/veri-bilimi-icin-is-ingilizcesi\/","title":{"rendered":"Veri Bilimi \u0130\u00e7in \u0130\u015f \u0130ngilizcesi"},"content":{"rendered":"
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Veri bilimi i\u00e7in i\u015f \u0130ngilizcesi, veri bilimi projeleriyle ilgili terimlerin ve kavramlar\u0131n \u0130ngilizce olarak kullan\u0131lmas\u0131d\u0131r. Bu, veri toplama, veri temizleme, model olu\u015fturma, sonu\u00e7lar\u0131n sunumu gibi ad\u0131mlar\u0131n her birinde ge\u00e7en terimleri anlamay\u0131 ve kullanmay\u0131 i\u00e7erir. Ayr\u0131ca, veri bilimi ile ilgili raporlar\u0131n yaz\u0131lmas\u0131, sunumlar\u0131n haz\u0131rlanmas\u0131 ve i\u015f birimleriyle ileti\u015fimde kullan\u0131lan \u0130ngilizce de bu kavram\u0131n bir par\u00e7as\u0131d\u0131r. Bu, veri bilimi uzmanlar\u0131n\u0131n uluslararas\u0131 bir ortamda \u00e7al\u0131\u015fabilmesini, meslekta\u015flar\u0131yla ve payda\u015flar\u0131yla etkili bir \u015fekilde ileti\u015fim kurabilmesini sa\u011flamaktad\u0131r.<\/p>\n\n\t\t<\/div>\n\t<\/div>\n<\/div><\/div><\/div><\/div>

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Veri Bilimi \u0130\u00e7in \u0130\u015f \u0130ngilizcesi Kelimeleri<\/strong><\/p>\n

Veri biliminin \u0130ngilizce kar\u015f\u0131l\u0131\u011f\u0131 \u201cdata science\u201d olarak bilinir. Data Science yani veri bilimi, b\u00fcy\u00fck veri k\u00fcmelerini analiz etmek ve bu analizlerden anlaml\u0131 bilgiler elde etmek i\u00e7in istatistiksel ve matematiksel y\u00f6ntemlerin yan\u0131 s\u0131ra bilgisayar bilimi tekniklerini kullanan disiplindir. Veri bilimi, veri madencili\u011fi, makine \u00f6\u011frenimi, veri analiti\u011fi ve b\u00fcy\u00fck veri gibi alt alanlar\u0131 i\u00e7erebilir. Bu disiplin, i\u015fletmelerin, bilim adamlar\u0131n\u0131n ve karar vericilerin, b\u00fcy\u00fck miktardaki verilerden de\u011ferli bilgiler \u00e7\u0131kararak daha iyi kararlar almas\u0131na yard\u0131mc\u0131 olur.<\/p>\n

A\u015fa\u011f\u0131daki listemizde sizler i\u00e7in Veri bilimi i\u00e7in i\u015f \u0130ngilizcesinde en s\u0131k kullan\u0131lan kelimeleri haz\u0131rlad\u0131k:<\/p>\n\n\n\n\n\t\n\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t
\u0130ngilizce<\/th>T\u00fcrk\u00e7e<\/th>\n<\/tr>\n<\/thead>\n
Algorithmic Trading<\/td>Algoritmik Ticaret<\/td>\n<\/tr>\n
Algorithm<\/td>Algoritma<\/td>\n<\/tr>\n
Analysis<\/td>Analiz<\/td>\n<\/tr>\n
Analytics<\/td>Analitik<\/td>\n<\/tr>\n
Anomaly Detection<\/td>Anomali Tespiti<\/td>\n<\/tr>\n
API<\/td>Uygulama Programlama Aray\u00fcz\u00fc<\/td>\n<\/tr>\n
Artificial Intelligence<\/td>Yapay Zeka<\/td>\n<\/tr>\n
Association Rule Learning<\/td>\u0130li\u015fki Kural\u0131 \u00d6\u011frenimi<\/td>\n<\/tr>\n
Batch Processing<\/td>Toplu \u0130\u015fleme<\/td>\n<\/tr>\n
Bias-Variance Tradeoff<\/td>\u00d6nyarg\u0131-Varyans Dengelemesi<\/td>\n<\/tr>\n
Big Data<\/td>B\u00fcy\u00fck Veri<\/td>\n<\/tr>\n
Bootstrapping<\/td>Bootstrapping<\/td>\n<\/tr>\n
Business Intelligence<\/td>\u0130\u015f Zekas\u0131<\/td>\n<\/tr>\n
Causal Inference<\/td>Neden-Sonu\u00e7 \u00c7\u0131kar\u0131m\u0131<\/td>\n<\/tr>\n
Classification<\/td>S\u0131n\u0131fland\u0131rma<\/td>\n<\/tr>\n
Cloud Computing<\/td>Bulut Bili\u015fim<\/td>\n<\/tr>\n
Clustering<\/td>K\u00fcmeleme<\/td>\n<\/tr>\n
Collaborative Filtering<\/td>\u0130\u015fbirlik\u00e7i Filtreleme<\/td>\n<\/tr>\n
Convolutional Neural Network<\/td>Evri\u015fimli Sinir A\u011f\u0131<\/td>\n<\/tr>\n
Correlation<\/td>Korelasyon<\/td>\n<\/tr>\n
Cross-Validation<\/td>\u00c7apraz Do\u011frulama<\/td>\n<\/tr>\n
Data<\/td>Veri<\/td>\n<\/tr>\n
Data Analysis<\/td>Veri Analizi<\/td>\n<\/tr>\n
Data Cleaning<\/td>Veri Temizleme<\/td>\n<\/tr>\n
Data Engineer<\/td>Veri M\u00fchendisi<\/td>\n<\/tr>\n
Data Mining<\/td>Veri Madencili\u011fi<\/td>\n<\/tr>\n
Data Model<\/td>Veri Modeli<\/td>\n<\/tr>\n
Data Science<\/td>Veri Bilimi<\/td>\n<\/tr>\n
Data Scientist<\/td>Veri Bilimci<\/td>\n<\/tr>\n
Data Visualization<\/td>Veri G\u00f6rselle\u015ftirme<\/td>\n<\/tr>\n
Database<\/td>Veritaban\u0131<\/td>\n<\/tr>\n
Decision Support Systems<\/td>Karar Destek Sistemleri<\/td>\n<\/tr>\n
Decision Tree<\/td>Karar A\u011fac\u0131<\/td>\n<\/tr>\n
Deep Learning<\/td>Derin \u00d6\u011frenme<\/td>\n<\/tr>\n
Descriptive Analytics<\/td>Betimleyici Analitik<\/td>\n<\/tr>\n
Dimensionality Reduction<\/td>Boyut Azaltma<\/td>\n<\/tr>\n
ETL (Extract, Transform, Load)<\/td>Veri \u00c7\u0131kartma, D\u00f6n\u00fc\u015ft\u00fcrme, Y\u00fckleme<\/td>\n<\/tr>\n
Ensemble Learning<\/td>Topluluk \u00d6\u011frenmesi<\/td>\n<\/tr>\n
Exploratory Data Analysis<\/td>Ke\u015fifsel Veri Analizi<\/td>\n<\/tr>\n
Exploration<\/td>Ke\u015fif<\/td>\n<\/tr>\n
Fault Tolerance<\/td>Hata Tolerans\u0131<\/td>\n<\/tr>\n
Feature Engineering<\/td>\u00d6zellik M\u00fchendisli\u011fi<\/td>\n<\/tr>\n
Feature Extraction<\/td>\u00d6zellik \u00c7\u0131kar\u0131m\u0131<\/td>\n<\/tr>\n
Feature Selection<\/td>\u00d6zellik Se\u00e7imi<\/td>\n<\/tr>\n
Forecasting<\/td>Tahmin<\/td>\n<\/tr>\n
Grid Search<\/td>K\u0131lavuz Arama<\/td>\n<\/tr>\n
Hadoop<\/td>B\u00fcy\u00fck Veri \u0130\u015fleme Platformu<\/td>\n<\/tr>\n
Hypothesis Testing<\/td>Hipotez Testi<\/td>\n<\/tr>\n
Imputation<\/td>Tamamlama<\/td>\n<\/tr>\n
Inference<\/td>\u00c7\u0131kar\u0131m<\/td>\n<\/tr>\n
Insight<\/td>G\u00f6r\u00fc\u015f<\/td>\n<\/tr>\n
K-Means Clustering<\/td>K-Ortalama K\u00fcmeleme<\/td>\n<\/tr>\n
Latent Dirichlet Allocation<\/td>Gizil Dirichlet Da\u011f\u0131l\u0131m\u0131<\/td>\n<\/tr>\n
Logistic Regression<\/td>Lojistik Regresyon<\/td>\n<\/tr>\n
Machine Learning<\/td>Makine \u00d6\u011frenimi<\/td>\n<\/tr>\n
Market Basket Analysis<\/td>Pazar Sepeti Analizi<\/td>\n<\/tr>\n
Model<\/td>Model<\/td>\n<\/tr>\n
Model Evaluation<\/td>Model De\u011ferlendirmesi<\/td>\n<\/tr>\n
Naive Bayes<\/td>Saf Bayes<\/td>\n<\/tr>\n
Natural Language Processing<\/td>Do\u011fal Dil \u0130\u015fleme<\/td>\n<\/tr>\n
Neural Network<\/td>Yapay Sinir A\u011f\u0131<\/td>\n<\/tr>\n
Normalization<\/td>Normalizasyon<\/td>\n<\/tr>\n
Outlier Detection<\/td>Ayk\u0131r\u0131 G\u00f6zlem Tespiti<\/td>\n<\/tr>\n
Pattern Recognition<\/td>Desen Tan\u0131ma<\/td>\n<\/tr>\n
PCA (Principal Component Analysis)<\/td>Temel Bile\u015fen Analizi<\/td>\n<\/tr>\n
Performance Metrics<\/td>Performans \u00d6l\u00e7\u00fctleri<\/td>\n<\/tr>\n
Pipelines<\/td>Pipeliner<\/td>\n<\/tr>\n
Predictive Analytics<\/td>Tahminsel Analitik<\/td>\n<\/tr>\n
Preprocessing<\/td>\u00d6n \u0130\u015fleme<\/td>\n<\/tr>\n
Principal Component Analysis<\/td>Temel Bile\u015fen Analizi<\/td>\n<\/tr>\n
Recommender Systems<\/td>Tavsiye Sistemleri<\/td>\n<\/tr>\n
Regularization<\/td>D\u00fczenleme<\/td>\n<\/tr>\n
Regression Analysis<\/td>Regresyon Analizi<\/td>\n<\/tr>\n
Reinforcement Learning<\/td>Takviyeli \u00d6\u011frenme<\/td>\n<\/tr>\n
ROC Curve<\/td>ROC E\u011frisi<\/td>\n<\/tr>\n
Sampling<\/td>\u00d6rnekleme<\/td>\n<\/tr>\n
Sentiment Analysis<\/td>Duygu Analizi<\/td>\n<\/tr>\n
Spark<\/td>Spark<\/td>\n<\/tr>\n
SQL (Structured Query Language)<\/td>Yap\u0131land\u0131r\u0131lm\u0131\u015f Sorgu Dili<\/td>\n<\/tr>\n
Streaming Data<\/td>Ak\u0131\u015f Verisi<\/td>\n<\/tr>\n
Supervised Learning<\/td>Denetimli \u00d6\u011frenme<\/td>\n<\/tr>\n
Support Vector Machine<\/td>Destek Vekt\u00f6r Makinesi<\/td>\n<\/tr>\n
Statistical Analysis<\/td>\u0130statistiksel Analiz<\/td>\n<\/tr>\n
Text Mining<\/td>Metin Madencili\u011fi<\/td>\n<\/tr>\n
Time Series Analysis<\/td>Zaman Serisi Analizi<\/td>\n<\/tr>\n
Transfer Learning<\/td>Transfer \u00d6\u011frenme<\/td>\n<\/tr>\n
Unsupervised Learning<\/td>Denetimsiz \u00d6\u011frenme<\/td>\n<\/tr>\n
Validation Set<\/td>Do\u011frulama Seti<\/td>\n<\/tr>\n
Variance<\/td>Varyans<\/td>\n<\/tr>\n
Web Scraping<\/td>Web Kaz\u0131ma<\/td>\n<\/tr>\n
Word Embeddings<\/td>Kelime G\u00f6m\u00fclme<\/td>\n<\/tr>\n
Zero Defect<\/td>S\u0131f\u0131r Hata<\/td>\n<\/tr>\n
Zone Control<\/td>B\u00f6lge Kontrol\u00fc<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\t\t<\/div>\n\t<\/div>\n<\/div><\/div><\/div><\/div>
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\u015eimdi bu kelimelerin baz\u0131lar\u0131n\u0131 c\u00fcmle i\u00e7inde inceleyelim:<\/p>\n

Artificial intelligence<\/strong> algorithms are used to automate tasks that would typically require human intelligence.
\n (Yapay zeka algoritmalar\u0131, genellikle insan zekas\u0131 gerektiren g\u00f6revleri otomatikle\u015ftirmek i\u00e7in kullan\u0131l\u0131r.)<\/em><\/p>\n

Big data<\/strong> analytics help organizations extract insights from large and complex datasets.
\n(B\u00fcy\u00fck veri analiti\u011fi, organizasyonlar\u0131n b\u00fcy\u00fck ve karma\u015f\u0131k veri k\u00fcmelerinden i\u00e7g\u00f6r\u00fcler \u00e7\u0131karmalar\u0131na yard\u0131mc\u0131 olur.)<\/em><\/p>\n

Classification <\/strong>algorithms categorize data into predefined classes or labels based on their features.
\n (S\u0131n\u0131fland\u0131rma algoritmalar\u0131, verileri \u00f6zelliklerine dayanarak \u00f6nceden tan\u0131mlanm\u0131\u015f s\u0131n\u0131flara veya etiketlere ay\u0131r\u0131r.)<\/em><\/p>\n

Clustering<\/strong> algorithms group similar data points together to discover inherent patterns and structures.
\n(K\u00fcmeleme algoritmalar\u0131, benzer veri noktalar\u0131n\u0131 bir araya getirerek i\u00e7sel desenleri ve yap\u0131lar\u0131 ke\u015ffeder.)<\/em><\/p>\n

Data analysis<\/strong> involves examining, cleaning, transforming, and modeling data to extract useful information.
\n(Veri analizi, kullan\u0131\u015fl\u0131 bilgiler \u00e7\u0131karmak i\u00e7in verilerin incelenmesini, temizlenmesini, d\u00f6n\u00fc\u015ft\u00fcr\u00fclmesini ve modellenmesini i\u00e7erir.) <\/em><\/p>\n

Data cleansing <\/strong>processes remove errors, inconsistencies, and duplicate entries from datasets.
\n(Veri temizleme s\u00fcre\u00e7leri, veri k\u00fcmelerinden hatalar\u0131, tutars\u0131zl\u0131klar\u0131 ve yinelenen giri\u015fleri kald\u0131r\u0131r.)<\/em><\/p>\n

A data engineer <\/strong>designs, develops, and maintains the architecture for data generation and processing.
\n(Bir veri m\u00fchendisi, veri \u00fcretimi ve i\u015fleme mimarisini tasarlar, geli\u015ftirir ve s\u00fcrd\u00fcr\u00fcr.)<\/em><\/p>\n

Data mining<\/strong> techniques uncover hidden patterns and relationships in large datasets.
\n(Veri madencili\u011fi teknikleri, b\u00fcy\u00fck veri k\u00fcmelerinde gizli desenleri ve ili\u015fkileri ortaya \u00e7\u0131kar\u0131r.) <\/em><\/p>\n

A data model <\/strong>represents the structure and relationships of data elements in a database.
\n(Bir veri modeli, bir veritaban\u0131ndaki veri \u00f6\u011felerinin yap\u0131s\u0131n\u0131 ve ili\u015fkilerini temsil eder.)<\/em><\/p>\n

Data science<\/strong> combines statistics, programming, and domain knowledge to analyze and interpret complex datasets.
\n (Veri bilimi, istatistik, programlama ve alan bilgisini birle\u015ftirerek karma\u015f\u0131k veri k\u00fcmelerini analiz etmek ve yorumlamak i\u00e7in kullan\u0131l\u0131r.)<\/em><\/p>\n

A data scientist<\/strong> uses analytical skills and domain knowledge to derive insights and make data-driven decisions.
\n(Bir veri bilimcisi, i\u00e7g\u00f6r\u00fcler elde etmek ve veriye dayal\u0131 kararlar almak i\u00e7in analitik beceriler ve alan bilgisinden yararlan\u0131r.) <\/em><\/p>\n

Data visualization tools<\/strong> help to present complex datasets in a visually appealing and understandable manner.
\n(Veri g\u00f6rselle\u015ftirme ara\u00e7lar\u0131, karma\u015f\u0131k veri k\u00fcmelerini g\u00f6rsel olarak \u00e7ekici ve anla\u015f\u0131labilir bir \u015fekilde sunmaya yard\u0131mc\u0131 olur.)<\/em><\/p>\n

Deep learning algorithms<\/strong> mimic the human brain’s neural networks to learn and make predictions from data.
\n (Derin \u00f6\u011frenme algoritmalar\u0131, insan beyninin sinir a\u011flar\u0131n\u0131 taklit ederek verilerden \u00f6\u011frenir ve tahminler yapar.)<\/em><\/p>\n

Feature engineering <\/strong>involves selecting, transforming, and creating features from raw data to improve model performance.
\n(\u00d6zellik m\u00fchendisli\u011fi, model performans\u0131n\u0131 art\u0131rmak i\u00e7in ham veriden \u00f6zellik se\u00e7me, d\u00f6n\u00fc\u015ft\u00fcrme ve olu\u015fturma i\u015flemlerini i\u00e7erir.)<\/em><\/p>\n

Machine learning algorithms<\/strong> enable computers to learn from data and make predictions or decisions without being explicitly programmed.
\n(Makine \u00f6\u011frenimi algoritmalar\u0131, bilgisayarlar\u0131n verilerden \u00f6\u011frenmesini ve a\u00e7\u0131k\u00e7a programlanmam\u0131\u015f tahminler veya kararlar yapmas\u0131n\u0131 sa\u011flar.)<\/em><\/p>\n

Natural language<\/strong> processing focuses on enabling computers to understand, interpret, and generate human language.
\n(Do\u011fal dil i\u015fleme, bilgisayarlar\u0131n insan dilini anlamas\u0131n\u0131, yorumlamas\u0131n\u0131 ve \u00fcretmesini sa\u011flamaya odaklan\u0131r.)<\/em><\/p>\n

A neural network<\/strong> is a computational model inspired by the human brain’s interconnected neurons, used for pattern recognition and machine learning tasks. (Bir sinir a\u011f\u0131, insan beyninin birbiriyle ba\u011flant\u0131l\u0131 sinir h\u00fccrelerinden esinlenerek olu\u015fturulan bir hesaplama modelidir; desen tan\u0131ma ve makine \u00f6\u011frenimi g\u00f6revleri i\u00e7in kullan\u0131l\u0131r.)<\/em><\/p>\n

Predictive analytics<\/strong> uses historical data and statistical techniques to forecast future trends or outcomes.
\n(Tahmine dayal\u0131 analitik, ge\u00e7mi\u015f verileri ve istatistiksel teknikleri kullanarak gelecek trendler veya sonu\u00e7lar\u0131 tahmin etmek i\u00e7in kullan\u0131l\u0131r.)<\/em><\/p>\n

Regression analysis<\/strong> examines the relationship between one or more independent variables and a dependent variable to predict future outcomes. (Regresyon analizi, bir veya daha fazla ba\u011f\u0131ms\u0131z de\u011fi\u015fken ile bir ba\u011f\u0131ml\u0131 de\u011fi\u015fken aras\u0131ndaki ili\u015fkiyi inceleyerek gelecekteki sonu\u00e7lar\u0131 tahmin etmeye \u00e7al\u0131\u015f\u0131r.)<\/em><\/p>\n

Statistical analysis<\/strong> involves using mathematical techniques to analyze data and draw conclusions about populations or samples.
\n(\u0130statistiksel analiz, verileri analiz etmek ve pop\u00fclasyonlar veya \u00f6rnekler hakk\u0131nda sonu\u00e7lar \u00e7\u0131karmak i\u00e7in matematiksel teknikler kullanmay\u0131 i\u00e7erir.)<\/p>\n\n\t\t<\/div>\n\t<\/div>\n<\/div><\/div><\/div><\/div>

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Veri Bilmi \u0130\u00e7in \u0130ngilizce Bilmenin \u00d6nemi<\/strong><\/p>\n

Veri bilimi i\u00e7in \u0130ngilizce bilmek \u00f6nemlidir \u00e7\u00fcnk\u00fc:
\nVeri bilimi alan\u0131nda bir\u00e7ok kaynak, makale, kitap ve e\u011fitim materyali genellikle \u0130ngilizce olarak kar\u015f\u0131n\u0131za \u00e7\u0131kar. \u0130ngilizce bilmek, bu kaynaklara eri\u015fimi kolayla\u015ft\u0131r\u0131r ve en son bilgilere ula\u015fman\u0131z\u0131 sa\u011flar.<\/p>\n

Veri bilimi, uluslararas\u0131 bir aland\u0131r ve \u0130ngilizce bilmek, k\u00fcresel \u015firketlerde \u00e7al\u0131\u015fma ve uluslararas\u0131 projelerde yer alma f\u0131rsatlar\u0131n\u0131 art\u0131rabilir. Ayr\u0131ca, \u00e7o\u011fu veri bilimi pozisyonu, \u0130ngilizce ileti\u015fim becerilerini gerektirmektedir.
\nVeri bilimi, istatistik, makine \u00f6\u011frenimi, b\u00fcy\u00fck veri teknolojileri gibi teknik alanlara dayan\u0131r. Bu alanlardaki kavramlar ve terimler genellikle \u0130ngilizce olarak kullan\u0131l\u0131r, bu nedenle \u0130ngilizce bilmek, teknik literat\u00fcr\u00fc anlaman\u0131za yard\u0131mc\u0131 olur.<\/p>\n

Sonu\u00e7 olarak, veri bilimi i\u00e7in \u0130ngilizce bilmek, kaynak eri\u015fimi, kariyer f\u0131rsatlar\u0131, uluslararas\u0131 i\u015fbirli\u011fi, teknik terimlerin anla\u015f\u0131lmas\u0131 ve k\u00fcresel standartlara uyum gibi bir\u00e7ok avantaj sa\u011flar. Bu nedenle, veri bilimi alan\u0131nda ba\u015far\u0131l\u0131 olmak isteyenler i\u00e7in \u0130ngilizce bilmenin \u00f6nemi \u00e7ok b\u00fcy\u00fckt\u00fcr.<\/p>\n\n\t\t<\/div>\n\t<\/div>\n<\/div><\/div><\/div><\/div>

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Veri Bilimi \u0130\u00e7in \u0130ngilizce Bilmek \u015eart M\u0131?<\/strong><\/p>\n

Veri bilimi i\u00e7in \u0130ngilizce bilmek, zorunlu bir gereklilik de\u011fildir, ancak bir\u00e7ok avantaj\u0131 vard\u0131r. Baz\u0131 \u015firketler ve projeler, yerel dilde \u00e7al\u0131\u015fmay\u0131 tercih edebilir veya uluslararas\u0131 bir ortamda \u00e7al\u0131\u015fma f\u0131rsatlar\u0131 sunmayabilir. \u00d6nemli olan, veri bilimi becerilerini geli\u015ftirmek ve ilgili teknik konularda uzmanla\u015fmakt\u0131r. Bu y\u00fczden de \u0130ngilizce \u00f6\u011frenmenin en do\u011fru adresi olan EnglishCentral\u2019a hemen kay\u0131t olun!<\/p>\n

EnglishCentral\u2019da uzman \u0130ngilizce \u00f6\u011fretmenleriyle ger\u00e7ekle\u015ftirece\u011finiz \u00f6zel derslerde i\u015f \u0130ngilizcesi konusunda sunum ve toplant\u0131 becerilerinizi geli\u015ftirebilir veya kendinizi \u0130ngilizce olarak ifade etme prati\u011fi yapabilirsiniz. Bunun yan\u0131 s\u0131ra, i\u015f \u0130ngilizcesiyle ilgili videolar\u0131 ve kurslar\u0131 izleyebilir, kelime \u00e7al\u0131\u015fmalar\u0131 yapabilir ve i\u015f \u0130ngilizcesiyle ilgili konularda grup derslerine kat\u0131labilirsiniz. \u00dcstelik EnglishCentral\u2019a kay\u0131t oldu\u011funuzda ilk ders \u00fccretsiz!<\/p>\n\n\t\t<\/div>\n\t<\/div>\n<\/div><\/div><\/div><\/div>

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Veri Bilimi Alan\u0131nda \u00c7al\u0131\u015fanlar Neden \u0130ngilizce \u00d6\u011frenmeli?<\/strong><\/p>\n

Veri bilimi i\u00e7in Veri bilimi alan\u0131nda \u00e7al\u0131\u015fanlar k\u00fcresel bir \u015firkette veya uluslararas\u0131 projelerde \u00e7al\u0131\u015fabilmek i\u00e7in ya da en basitinden kaynaklara kolayca ula\u015fabilmek i\u00e7in \u0130ngilizce \u00f6\u011frenmelidirler.<\/p>\n

EnglishCentral\u2019da farkl\u0131 seviyelere ve ilgi alanlar\u0131na uygun olarak tan\u0131ml\u0131 olan 30.000’den fazla \u0130ngilizce video bulunmaktad\u0131r. T\u00fcm videolar\u0131m\u0131zda bulunan \u0130zle, \u00d6\u011fren, Konu\u015f ad\u0131mlar\u0131n\u0131 izleyerek ki\u015fisel \u00e7al\u0131\u015fman\u0131z\u0131 \u015fekillendirebilir ve kelime \u00f6\u011frenme ara\u00e7lar\u0131 ve quizlerle kelime da\u011farc\u0131\u011f\u0131n\u0131z\u0131 geli\u015ftirebilir, \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131z videolarla ilgili olarak \u00f6\u011fretmenlerle birebir g\u00f6r\u00fc\u015fmeler yapabilirsiniz. Sadece siteyle ya da uygulamayla kalmay\u0131p ayn\u0131 zamanda Youtube’dan izleyerek dinleme prati\u011fi yapabilir ve e\u011flenceli, ilgi \u00e7ekici videolarla \u0130ngilizce \u00f6\u011frenebilirsiniz. Ayr\u0131ca, Spotify’da EnglishCentral ile \u0130ngilizce \u00d6\u011fren podcastini dinleyerek, otob\u00fcste, yolda veya herhangi bir yerde \u0130ngilizcenizi geli\u015ftirebilirsiniz.<\/p>\n\n\t\t<\/div>\n\t<\/div>\n<\/div><\/div><\/div><\/div>

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Veri Bilimi \u0130\u00e7in \u0130\u015f \u0130ngilizcesi Hakk\u0131nda S\u0131k Sorulan Sorular<\/p>\n

Veri bilimci olmak i\u00e7in hangi dil gerekir?<\/p>\n

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Programlama dili gerekmektedir. \u00c7e\u015fitli programlama dilleri vard\u0131r: Bunlardan en \u00e7ok tercih edilenleri R ve Python gibi programlama dilleridir.<\/p>\n

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Veri bilimi projelerinde hangi t\u00fcr i\u015f \u0130ngilizcesi kullan\u0131l\u0131r?<\/p>\n

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Veri bilimi projelerinde kullan\u0131lan i\u015f \u0130ngilizcesi, projenin her a\u015famas\u0131nda kullan\u0131lan terimleri i\u00e7erebilir. \u00d6rne\u011fin, veri toplama s\u00fcrecinde “data collection”, model olu\u015fturma a\u015famas\u0131nda “model building” gibi terimler yayg\u0131n olarak kullan\u0131l\u0131r.<\/p>\n

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Veri bilimi becerileri nelerdir?<\/p>\n

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Veri bilimi becerileri, istatistik, programlama dilleri (Python, R, SQL), veri g\u00f6rselle\u015ftirme, makine \u00f6\u011frenimi, derin \u00f6\u011frenme, b\u00fcy\u00fck veri teknolojileri ve veri taban\u0131 y\u00f6netimi gibi alanlar\u0131 i\u00e7erebilir.<\/p>\n

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Hangi sekt\u00f6rlerde veri bilimi kullan\u0131l\u0131r?<\/p>\n

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Veri bilimi, finans, sa\u011fl\u0131k, perakende, pazarlama, end\u00fcstriyel \u00fcretim, otomotiv ve daha bir\u00e7ok sekt\u00f6rde kullan\u0131l\u0131r ve \u00e7e\u015fitli i\u015flevlerde de\u011fer sa\u011flar.<\/p>\n