標(biāo)題: Titlebook: ; [打印本頁(yè)] 作者: Falter 時(shí)間: 2025-3-21 17:42
書目名稱Guide to Teaching Data Science影響因子(影響力)
書目名稱Guide to Teaching Data Science影響因子(影響力)學(xué)科排名
書目名稱Guide to Teaching Data Science網(wǎng)絡(luò)公開(kāi)度
書目名稱Guide to Teaching Data Science網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書目名稱Guide to Teaching Data Science被引頻次
書目名稱Guide to Teaching Data Science被引頻次學(xué)科排名
書目名稱Guide to Teaching Data Science年度引用
書目名稱Guide to Teaching Data Science年度引用學(xué)科排名
書目名稱Guide to Teaching Data Science讀者反饋
書目名稱Guide to Teaching Data Science讀者反饋學(xué)科排名
作者: Devastate 時(shí)間: 2025-3-21 21:39
What is Data Science?gle, consensus definition for data science is its multifaceted nature: it can be described as a science, as a research method, as a discipline, as a workflow, or as a profession. One single definition just cannot capture this diverse essence of data science. In this chapter, we first take an interdi作者: 注射器 時(shí)間: 2025-3-22 04:05
Data Science Thinkingonents of data science, and describes the contribution of each one to data thinking—the mode of thinking required of data scientists (not only professional ones). Indeed, data science thinking integrates the thinking modes associated with the various disciplines that make up data science. Specifical作者: 鑒賞家 時(shí)間: 2025-3-22 08:24 作者: 親愛(ài) 時(shí)間: 2025-3-22 09:12
Opportunities in Data Science Educational-world context (Sect.?.), teaching STEM with real-world data (Sect.?.), bridging gender gaps in STEM education (Sect.?.), teaching twenty-first century skills (Sect.?.), interdisciplinary pedagogy (Sect.?.), and professional development for teachers (Sect.?.). We conclude with an interdisciplinary作者: 治愈 時(shí)間: 2025-3-22 14:55 作者: 治愈 時(shí)間: 2025-3-22 20:20 作者: STRIA 時(shí)間: 2025-3-23 01:15
Data Science as a Research Methodhe research process that data science inspires (Sect.?.). Then, Sect.?. presents examples of cognitive, organizational, and technological skills which are important for coping with the challenge of data science as a research method, and Sect.?. highlights pedagogical methods for coping with it. In t作者: 織物 時(shí)間: 2025-3-23 03:49 作者: 考古學(xué) 時(shí)間: 2025-3-23 08:50 作者: endoscopy 時(shí)間: 2025-3-23 09:48
Professional Skills and Soft Skills in Data Scienceofessional skills (Sect.?.) and soft skills (Sect.?.). Professional skills are specific skills that are needed in order to engage in data science, while soft skills are more general skills that acquire unique importance in the context of data science. In each section, we address both cognitive, orga作者: dainty 時(shí)間: 2025-3-23 16:39 作者: thyroid-hormone 時(shí)間: 2025-3-23 18:39 作者: incredulity 時(shí)間: 2025-3-23 23:19 作者: 經(jīng)典 時(shí)間: 2025-3-24 02:37
Machine Learning Algorithmsyze them from a pedagogical perspective. The algorithms we discuss are the K-nearest neighbors (KNN) (Sect.?.), decision trees (Sect.?.), Perceptron (Sect.?.), linear regression (Sect.?.), logistic regression (Sect.?.), and neural networks (Sect.?.). Finally, we discuss interrelations between the in作者: jet-lag 時(shí)間: 2025-3-24 07:11
https://doi.org/10.1007/978-3-662-04698-2a science programs for a variety of learners from a variety of disciplines (data science, computer science, statistics, engineering, life science, social science and humanities) and a variety of levels (from school children to academia and industry). While significant efforts are being invested in t作者: 手榴彈 時(shí)間: 2025-3-24 10:52
September-November: the Approach of War,gle, consensus definition for data science is its multifaceted nature: it can be described as a science, as a research method, as a discipline, as a workflow, or as a profession. One single definition just cannot capture this diverse essence of data science. In this chapter, we first take an interdi作者: lesion 時(shí)間: 2025-3-24 17:17 作者: 古董 時(shí)間: 2025-3-24 22:49 作者: giggle 時(shí)間: 2025-3-25 01:43
Outward Remittances from the Gulf,al-world context (Sect.?.), teaching STEM with real-world data (Sect.?.), bridging gender gaps in STEM education (Sect.?.), teaching twenty-first century skills (Sect.?.), interdisciplinary pedagogy (Sect.?.), and professional development for teachers (Sect.?.). We conclude with an interdisciplinary作者: 有毛就脫毛 時(shí)間: 2025-3-25 06:46
Shuvechha Ghimire,Shumaila Fatima, challenging from an educational perspective (that is, in terms of curricula and pedagogy). In Chap. ., we discuss the challenge of integrating the application domain into data science education, and in this chapter, we elaborate on the challenges posed by the interdisciplinary structure of data sc作者: 不容置疑 時(shí)間: 2025-3-25 11:32 作者: 享樂(lè)主義者 時(shí)間: 2025-3-25 12:17
https://doi.org/10.1007/978-981-99-1494-4he research process that data science inspires (Sect.?.). Then, Sect.?. presents examples of cognitive, organizational, and technological skills which are important for coping with the challenge of data science as a research method, and Sect.?. highlights pedagogical methods for coping with it. In t作者: strdulate 時(shí)間: 2025-3-25 16:50
Temples and Political Development,he adoption of a new data science curriculum developed in Israel for high school computer science pupils, by high school computer science teachers. We analyze the adoption process using the diffusion of innovation and the crossing the chasm theories. Accordingly, we first present the diffusion of in作者: Receive 時(shí)間: 2025-3-25 22:55
https://doi.org/10.1007/978-1-349-10039-2educational perspective. First, we present several approaches to the data science workflow (Sect.?.), following which we elaborate on the pedagogical aspects of the different phases of the workflow: data collection (Sect.?.), data preparation (Sect.?.), exploratory data analysis (Sect.?.), modeling 作者: Cantankerous 時(shí)間: 2025-3-26 04:10 作者: cortisol 時(shí)間: 2025-3-26 04:21 作者: 疾馳 時(shí)間: 2025-3-26 10:06 作者: 違抗 時(shí)間: 2025-3-26 14:11 作者: Indurate 時(shí)間: 2025-3-26 19:35 作者: adumbrate 時(shí)間: 2025-3-27 00:19
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.?.).作者: Certainty 時(shí)間: 2025-3-27 02:26
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.?.).作者: 故意釣到白楊 時(shí)間: 2025-3-27 06:43
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.?.).作者: Kinetic 時(shí)間: 2025-3-27 10:17
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.?.).作者: Statins 時(shí)間: 2025-3-27 17:10
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.?.).作者: 沒(méi)有貧窮 時(shí)間: 2025-3-27 19:36
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.?.).作者: 弄皺 時(shí)間: 2025-3-28 00:17
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.?.).作者: vector 時(shí)間: 2025-3-28 03:32 作者: Infect 時(shí)間: 2025-3-28 07:04 作者: 全能 時(shí)間: 2025-3-28 13:49 作者: acheon 時(shí)間: 2025-3-28 17:15
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.?.).作者: 集中營(yíng) 時(shí)間: 2025-3-28 21:12 作者: 徹底檢查 時(shí)間: 2025-3-29 00:29
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.?.).作者: Indicative 時(shí)間: 2025-3-29 06:40 作者: exclusice 時(shí)間: 2025-3-29 08:20 作者: seroma 時(shí)間: 2025-3-29 14:38
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.?.).作者: 神圣將軍 時(shí)間: 2025-3-29 17:33
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.作者: Perennial長(zhǎng)期的 時(shí)間: 2025-3-29 20:34
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.作者: 無(wú)畏 時(shí)間: 2025-3-30 02:21
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.作者: 著名 時(shí)間: 2025-3-30 05:50
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.?.).作者: 難管 時(shí)間: 2025-3-30 11:43 作者: 紳士 時(shí)間: 2025-3-30 15:06 作者: 赦免 時(shí)間: 2025-3-30 18:57 作者: Constant 時(shí)間: 2025-3-30 22:53
The Pedagogical Challenge of Machine Learning Educationte 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.?.).作者: 錯(cuò) 時(shí)間: 2025-3-31 03:39
