Titlebook: ;

adumbrate

Opportunities in Data Science Educationury skills (Sect. .), interdisciplinary pedagogy (Sect. .), and professional development for teachers (Sect. .). We conclude with an interdisciplinary perspective on the opportunities of data science education (Sect. .).

Certainty

The Data Science Workflowaspects of the different phases of the workflow: data collection (Sect. .), data preparation (Sect. .), exploratory data analysis (Sect. .), modeling (Sect. .), and communication and action (Sect. .). We conclude with an interdisciplinary perspective on the data science workflow (Sect. .).

故意钓到白杨

Machine Learning AlgorithmsSect. .), linear regression (Sect. .), logistic regression (Sect. .), and neural networks (Sect. .). Finally, we discuss interrelations between the interdisciplinarity of data science and the teaching of ML algorithms (Sect. .).

Kinetic

https://doi.org/10.1057/978-1-137-40354-4ct. .), model complexity (Sect. .), overfitting and underfitting (Sect. .), loss function optimization and the gradient descent algorithm (Sect. .), and regularization (Sect. .). We conclude this chapter by emphasizing what ML core concepts should be discussed in the context of the application domain (Sect. .).

Statins

Core Concepts of Machine Learningct. .), model complexity (Sect. .), overfitting and underfitting (Sect. .), loss function optimization and the gradient descent algorithm (Sect. .), and regularization (Sect. .). We conclude this chapter by emphasizing what ML core concepts should be discussed in the context of the application domain (Sect. .).

没有贫穷

https://doi.org/10.1007/978-3-662-04698-2 principles we applied in it (Sect. .), its structure (Sect. .), and how it can be used by educators who teach data science in different educational frameworks (Sect. .). Finally, we present several main kinds of learning environments that are appropriate for teaching and learning data science (Sect. .).

弄皱

September-November: the Approach of War, (Sect. .), and data science as a profession (Sect. .). We conclude by highlighting three main characteristics of data science: interdisciplinarity, learner diversity, and its research-oriented nature (Sect. .).

vector

Infect

全能