Stepik О курсе
Подробнее
Please note: adaptive courses are no longer supported on Stepik
Right now Stepik is developing an adaptive learning engine which chooses content for each learner individually and give everyone individual learning path. In this course you can try out this adaptive engine and learn Data Science.
The difference between a regular and an adaptive course:
There is not set structure in an adaptive course.
You can not follow a linear pre-set structure of the course, the adaptive engine generates a unique path for each student.
In the course you'll have extra buttons on the top of the page like this:
The first button becomes active only after you successfully solve the problem. If the problem seems too difficult or too easy, you can click on one of the buttons on the right, the engine will take into account the knowledge level for future content recommendations.
Initially, the adaptive system may behave somewhat randomly for you, but the more problems you solve, the smarter it becomes!
Содержание
6 занятийПрограмма курса
№ТемаЧто внутри
01Data Science. Basic R.
Подробности будут доступны на странице курса
02Data Science. Data Analysis and Visualization with Python
Подробности будут доступны на странице курса
03Data Science. Logistic Regression
- Introduction to logistic regression
- Logistic regression in RStudio
- Exploring data
- The importance of data types
- Training and testing sets
- Simple logistic regression - 1
- Simple logistic regression - 2
- Statistical significance of covariates
- Computing odds ratios
- Interpreting odds ratios
- Confidence intervals - 1
- Confidence intervals - 2
- ORs for non-unitary increments
- Likelihood ratio test - 1
- Likelihood ratio test - 2
- Estimating probabilities
- From probability to prediction
- Assessing model calibration
- Adjusting model calibration
- Confusion matrix
- Sensitivity
- Specificity
04Data Science. Effect sizes
- Measures of difference: d-family effect sizes
- Measures of difference. Categorical and ordinal data
- Measures of association: r-family effect sizes
- Language skills
- Origami competition
- Time management training
- Movie rating
- Treatment efficiency
- Wine quality
- Cats' tails
- Pseudo-Ballmer's peak
- In the footsteps of Cuvier - 1
- Thumbles - 1
- Geography of black metal bands
- Who prefer hitchhiking
- Mobile application update
- In the footsteps of Cuvier - 2
- Finding a black cat
- Thumbles - 2
- Models comparison
- Depression and disorders
- Kaggle holywar
- Parseltongue skills
05Data Science. Introduction to Julia
- Julia. Intro
- Julia. Intro
- Julia. Intro
- Julia. Arrays
- Julia. Arrays
- Julia. Arrays
- Julia. Matrix
- Julia. Function Calls
- Expressions
- Julia. Logical operators
- Julia. Logical operators
- Exercises
- Julia
- An Introduction to Julia. Intro 1
- An Introduction to Julia. Intro 2
- Types of variables
- Julia. Arrays and matrices
- Theoretical lesson. Julia. Useful links
06Modeling and Simulation using R
- What is Discrete Event Simulation (DES) theory
- What is Discrete Event Simulation (DES)
- Key Components of DES theory
- Key Components of DES
- What is R? theory
- What is R?
- R concepts. theory
- R concepts.
- What is RStudio? theory
- What is RStudio?
- Installation of R. theory
- Installation of R.
- Installation of RStudio. theory
- Installation of RStudio.
- Installation of R Package "Simmer" theory
- Installation of R Package "Simmer"
- Piping ! theory
- Piping !
- Load Model into R. theory
- Load Model into R.
- Plot the Model Using R. theory
- Running Simulation and Data Analysis. theory
- Practical I
- Practical II
- Practical III
- Practical IV
- Practical V
- Practical VI
- Practical VII
- Practical VIII
01Data Science. Basic R.
02Data Science. Data Analysis and Visualization with Python
03Data Science. Logistic Regression
- Introduction to logistic regression
- Logistic regression in RStudio
- Exploring data
- The importance of data types
- Training and testing sets
- Simple logistic regression - 1
- Simple logistic regression - 2
- Statistical significance of covariates
- Computing odds ratios
- Interpreting odds ratios
- Confidence intervals - 1
- Confidence intervals - 2
- ORs for non-unitary increments
- Likelihood ratio test - 1
- Likelihood ratio test - 2
- Estimating probabilities
- From probability to prediction
- Assessing model calibration
- Adjusting model calibration
- Confusion matrix
- Sensitivity
- Specificity
04Data Science. Effect sizes
- Measures of difference: d-family effect sizes
- Measures of difference. Categorical and ordinal data
- Measures of association: r-family effect sizes
- Language skills
- Origami competition
- Time management training
- Movie rating
- Treatment efficiency
- Wine quality
- Cats' tails
- Pseudo-Ballmer's peak
- In the footsteps of Cuvier - 1
- Thumbles - 1
- Geography of black metal bands
- Who prefer hitchhiking
- Mobile application update
- In the footsteps of Cuvier - 2
- Finding a black cat
- Thumbles - 2
- Models comparison
- Depression and disorders
- Kaggle holywar
- Parseltongue skills
05Data Science. Introduction to Julia
- Julia. Intro
- Julia. Intro
- Julia. Intro
- Julia. Arrays
- Julia. Arrays
- Julia. Arrays
- Julia. Matrix
- Julia. Function Calls
- Expressions
- Julia. Logical operators
- Julia. Logical operators
- Exercises
- Julia
- An Introduction to Julia. Intro 1
- An Introduction to Julia. Intro 2
- Types of variables
- Julia. Arrays and matrices
- Theoretical lesson. Julia. Useful links
06Modeling and Simulation using R
- What is Discrete Event Simulation (DES) theory
- What is Discrete Event Simulation (DES)
- Key Components of DES theory
- Key Components of DES
- What is R? theory
- What is R?
- R concepts. theory
- R concepts.
- What is RStudio? theory
- What is RStudio?
- Installation of R. theory
- Installation of R.
- Installation of RStudio. theory
- Installation of RStudio.
- Installation of R Package "Simmer" theory
- Installation of R Package "Simmer"
- Piping ! theory
- Piping !
- Load Model into R. theory
- Load Model into R.
- Plot the Model Using R. theory
- Running Simulation and Data Analysis. theory
- Practical I
- Practical II
- Practical III
- Practical IV
- Practical V
- Practical VI
- Practical VII
- Practical VIII
Организатор
Автор курса
Мнения учеников
Отзывы о курсе
Оставьте отзыв
Расскажите о качестве обучения, поддержке и результате. Это поможет другим выбрать организацию осознанно.
Оставьте заявку
Консультант ответит на вопросы о курсе «Stepik Contest. Data Science» и поможет разобраться в деталях обучения.
Нажимая кнопку, вы даете согласие на обработку персональных данных
Направления:Data science
Информация обновлена 12 августа 2026 г.
Продолжить выбор
Похожие курсы
Начать без затрат
