IE 421 • Final • Data Science for Engineers
Eğitmen
Hasan Arcas
Veri Bilimci
Bilgisayar Mühendisliği bölümünü okurken, 3. sınıfta Veri Bilimi alanına aşık olmuştum. Mezun olana kadar hem dersler eşliğinde hem serbest bir şekilde çalışmaya devam ettim, şimdiyse profesyonel olarak bir özel yazılım şirketinde veri bilimci pozisyonunda çalışıp her geçen gün kendimi bu alanda yetiştirmeye devam ediyorum.
Paketi Tamamla
🎓 Bilgi Üniversitesi öğrencilerinin %92'si tüm paketi alarak çalışıyor.
Konular
Introducing Linear Models
Modelling
Vocabulary of Modelling
Fitting and Interpreting a Model
Parameter Estimation
Exercise 1
Categorical Predictors
Exercise 2
Data Preprocessing
What is Data Preprocessing?
Exercise 1
Exercise 2
Centering and Scaling
Exercise 3
Exercise 4
Standardization and Normalization
Exercise 5
Exercise 6
Skewness
Exercise 7
Data Reduction
Exercise 8
One-hot and Dummy Encoding
Exercise 9
Label Encoding
Exercise 10
Missing Values
Exercise 11
Data Leakage
Unbalanced Data
Exercise 12
Checking Data
Multiple Linear Regression
Checking Linear Relation
Transform Data for Linear Relationship
Multiple Linear Regression
Exercise 1
Numerical and Categorical Predictors
Main and Interaction Effects
Exercise 2
Comparing Models
Exercise 3
Model Evaluation
Data Exploration
Exercise 1
Preprocessing Step
Fitting a Model Using a Recipe
Exercise 2
Performance Evaluation
Cross Validation
Exercise 3
Principal Component Analysis (PCA)
What is PCA?
Apply PCA Step by Step
Packages for PCA
Exercise 1
Exercise 2
Exercise 3
Exercise 4
Quantifying Uncertainty
Statistical Inference and Confidence Interval
Bootstrapping
Confidence Interval
Exercise 1
Bootstrapping With tidymodels Package
Exercise 2
Accuracy vs Precision
Sample Final Problems
Final Question 1
Final Question 2
Final Question 3
Final Question 4
Final Question 5
Final Question 6
Final Question 7
Final Question 8
Final R Markdown Creation
Değerlendirmeler
Henüz hiç değerlendirme yok.
Sıkça Sorulan Sorular
Örneğin, Koç Üniversitesi - MATH 101 (Calculus) veya başka bir okulun benzer dersi olsun, paketlerimiz tam da o derse göre tasarlanır. Böylece nokta atışı çalışır, zaman kazanırsın.
Sınava özel videolar —konu anlatımları, çıkmış sorular ve çözümleri, özet notlar—içerir. Sınavda sıkça çıkan soruları hedefler. Eğitmenlerimiz, üniversitenin akademik takvimini takip ederek paketleri sürekli günceller. Böylece, gereksiz detaylarla vakit kaybetmeden başarını artırmaya odaklanabilirsin.
Ders İçeriği
Introducing Linear Models
Modelling
Vocabulary of Modelling
Fitting and Interpreting a Model
Parameter Estimation
Exercise 1
Categorical Predictors
Exercise 2
Data Preprocessing
What is Data Preprocessing?
Exercise 1
Exercise 2
Centering and Scaling
Exercise 3
Exercise 4
Standardization and Normalization
Exercise 5
Exercise 6
Skewness
Exercise 7
Data Reduction
Exercise 8
One-hot and Dummy Encoding
Exercise 9
Label Encoding
Exercise 10
Missing Values
Exercise 11
Data Leakage
Unbalanced Data
Exercise 12
Checking Data
Multiple Linear Regression
Checking Linear Relation
Transform Data for Linear Relationship
Multiple Linear Regression
Exercise 1
Numerical and Categorical Predictors
Main and Interaction Effects
Exercise 2
Comparing Models
Exercise 3
Model Evaluation
Data Exploration
Exercise 1
Preprocessing Step
Fitting a Model Using a Recipe
Exercise 2
Performance Evaluation
Cross Validation
Exercise 3
Principal Component Analysis (PCA)
What is PCA?
Apply PCA Step by Step
Packages for PCA
Exercise 1
Exercise 2
Exercise 3
Exercise 4
Quantifying Uncertainty
Statistical Inference and Confidence Interval
Bootstrapping
Confidence Interval
Exercise 1
Bootstrapping With tidymodels Package
Exercise 2
Accuracy vs Precision
Sample Final Problems
Final Question 1
Final Question 2
Final Question 3
Final Question 4
Final Question 5
Final Question 6
Final Question 7
Final Question 8
Final R Markdown Creation