Titlebook: ;

acheon

What is Data Science? (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. .).

集中营

彻底检查

The Pedagogical Chasm in Data Science Educationchallenge that reduces the motivation of the majority of teachers to adopt the curriculum) that slows down the adoption process of the innovation (Sect. .). Finally, we discuss the implications of the pedagogical chasm for data science education (Sect. .).

Indicative

exclusice

seroma

Refractive Error and School Eye HealthSect. .), 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. .).

神圣将军

https://doi.org/10.1007/978-981-99-1494-4iscussions about data science skills in this chapter and in Chap. . are especially important today due to the increasing awareness that scientists and engineers, in general, and data scientists, in particular, should acquire professional skills, in addition to disciplinary and technical knowledge.

Perennial长期的

Chinese and Soviet Political Strategies,. .). The discussion about data science skills is especially important today due to the increasing awareness of the fact that scientists and engineers in general, and data scientists in particular, should acquire professional and soft skills, in addition to disciplinary and technical knowledge.

无畏

t. .). We also present teaching methods that are especially appropriate for the teaching of social issues of data science (Sect. .). Throughout the chapter, we highlight the social perspective, which in turn further emphasizes the interdisciplinarity of data science.

著名

South and South East Asia, 1945-79te not being mentioned frequently in this part of the book, are important to be kept in mind in ML teaching processes (Sect. .). We conclude this chapter by highlighting the importance of ML education in the context of the application domain (Sect. .).