Курс на Stepik
Курс Data Science. Logistic Regression
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Data Science. Logistic Regression ★ 4.500

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In this course, you will learn what is a logistic regression model, what it is used for, and how to fit a model to real data using RStudio.

Показатель Текущие показатели Рост
Значение 🏆 Рейтинг 3 дн 7 дн 30 дн
Количество учеников на курсе «Data Science. Logistic Regression»Учеников на курсе 788
Сертификаты, выданные на курсе «Data Science. Logistic Regression»Сертификатов выдано 0
Отзывы о курсе «Data Science. Logistic Regression»Отзывов получено 2
Рейтинг курса «Data Science. Logistic Regression»Рейтинг курса 4.500
Уроки в курсе «Data Science. Logistic Regression»Количество уроков 22
Тесты в курсе «Data Science. Logistic Regression»Количество квизов 49
Время прохождения курса «Data Science. Logistic Regression»Время прохождения курса —
Обновления курса «Data Science. Logistic Regression»Обновления курса — —
Дата публикации курса «Data Science. Logistic Regression»Дата публикации курса — ———
Последнее обновление курса «Data Science. Logistic Regression»Последнее обновление — ———
4.500 ★
из 5
2 отзыва
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Chynara Bektursunova
Chynara Bektursunova •
★ ★ ★ ★ ★
• 5 лет назад

Course is interesting and well-organized. As for the new one who is dealing with R studio, it is quite primitive which is good and should be treated as advantage. However, the last modules should be checked as questions are structured not in a correct way, which is confusing. Also adding the guidance on how to install packages would be an advantage for this course.

Vasilii Feofanov
Vasilii Feofanov •
★ ★ ★ ★ ★
• 9 лет назад

It is a good course that can be considered as a practical guide for logistic regression using R. The course gives a recommendation how to make a model selection as well as how to interpret correctly coefficients of a model. For better understanding, two real datasets are used for exercises. The course does not provide to you deep theory or mathematical substantiation. So, I consider this course as a nice supplement to such materials as https://stepik.org/524 (in Russian) or any appropriate textbook (e.g. "Hastie et al. - The Elements of Statistical Learning").