Professor Orit Hazzan is a faculty member at the Technion’s Department of Education in Science and Technology since October 2000. Her research focuses on computer science, software engineering and data science education. Within this framework she researches cognitive and social processes on the individual, the team and the organization levels, in all kinds of organizations. She has published about 130 papers in professional refereed journals and conference proceedings, and seven books. In 2007-2010 she chaired the High School Computer Science Curriculum Committee assigned by the Israeli Ministry of Education. In 2011-2015 Hazzan was the faculty Dean. From 2017 to 2019, Hazzan served the Technion Dean of Undergraduate Studies. Dr. Koby Mike is a Ph.D. graduate from the Technion's Department of Education in Science and Technology under the supervision of Professor Orit Hazzan. He continued his a post-doc research on data science education at the Bar-Ilan University, and retains B.Sc. and an M.Sc. in Electrical Engineering from Tel Aviv University. After two decades of professional career is the Israeli hi-tech industry, he returned to academia for his doctoral studies on data science education. As part of is research, Koby developed and taught several data science programs for high school students, high school computer science teachers, and graduate students and researchers in social sciences and digital humanities.

• Part I: Overview of Data Science and Data Science Education.- Chapter 1. Introduction.- Chapter 2. What is data science.- Chapter 3. Introduction to data science education.- Chapter 4. Data science thinking.- Part II: Challenges of Data Science Education.- Chapter 5. The pedagogical challenge of data science education.- Chapter 6. Data science education and the variety of learners.- Chapter 7. The interdisciplinarity challenge.- Chapter 8. Data science skills.- Part III: Data science Teaching frameworks.- Chapter 9. Teacher Preparation - the Method for Teaching Data Science course.- Chapter 10. Data Science for Social Science.- Chapter 11. Conclusion.

Select Guide Rating

Professor Orit Hazzan is a faculty member at the Technion’s Department of Education in Science and Technology since October 2000. Her research focuses on computer science, software engineering and data science education. Within this framework she researches cognitive and social processes on the individual, the team and the organization levels, in all kinds of organizations. She has published about 130 papers in professional refereed journals and conference proceedings, and seven books. In 2007-2010 she chaired the High School Computer Science Curriculum Committee assigned by the Israeli Ministry of Education. In 2011-2015 Hazzan was the faculty Dean. From 2017 to 2019, Hazzan served the Technion Dean of Undergraduate Studies. Dr. Koby Mike is a Ph.D. graduate from the Technion's Department of Education in Science and Technology under the supervision of Professor Orit Hazzan. He continued his a post-doc research on data science education at the Bar-Ilan University, and retains B.Sc. and an M.Sc. in Electrical Engineering from Tel Aviv University. After two decades of professional career is the Israeli hi-tech industry, he returned to academia for his doctoral studies on data science education. As part of is research, Koby developed and taught several data science programs for high school students, high school computer science teachers, and graduate students and researchers in social sciences and digital humanities.

• Part I: Overview of Data Science and Data Science Education.- Chapter 1. Introduction.- Chapter 2. What is data science.- Chapter 3. Introduction to data science education.- Chapter 4. Data science thinking.- Part II: Challenges of Data Science Education.- Chapter 5. The pedagogical challenge of data science education.- Chapter 6. Data science education and the variety of learners.- Chapter 7. The interdisciplinarity challenge.- Chapter 8. Data science skills.- Part III: Data science Teaching frameworks.- Chapter 9. Teacher Preparation - the Method for Teaching Data Science course.- Chapter 10. Data Science for Social Science.- Chapter 11. Conclusion.

Select Guide Rating

Professor Orit Hazzan is a faculty member at the Technion’s Department of Education in Science and Technology since October 2000. Her research focuses on computer science, software engineering and data science education. Within this framework she researches cognitive and social processes on the individual, the team and the organization levels, in all kinds of organizations. She has published about 130 papers in professional refereed journals and conference proceedings, and seven books. In 2007-2010 she chaired the High School Computer Science Curriculum Committee assigned by the Israeli Ministry of Education. In 2011-2015 Hazzan was the faculty Dean. From 2017 to 2019, Hazzan served the Technion Dean of Undergraduate Studies. Dr. Koby Mike is a Ph.D. graduate from the Technion's Department of Education in Science and Technology under the supervision of Professor Orit Hazzan. He continued his a post-doc research on data science education at the Bar-Ilan University, and retains B.Sc. and an M.Sc. in Electrical Engineering from Tel Aviv University. After two decades of professional career is the Israeli hi-tech industry, he returned to academia for his doctoral studies on data science education. As part of is research, Koby developed and taught several data science programs for high school students, high school computer science teachers, and graduate students and researchers in social sciences and digital humanities.

• Part I: Overview of Data Science and Data Science Education.- Chapter 1. Introduction.- Chapter 2. What is data science.- Chapter 3. Introduction to data science education.- Chapter 4. Data science thinking.- Part II: Challenges of Data Science Education.- Chapter 5. The pedagogical challenge of data science education.- Chapter 6. Data science education and the variety of learners.- Chapter 7. The interdisciplinarity challenge.- Chapter 8. Data science skills.- Part III: Data science Teaching frameworks.- Chapter 9. Teacher Preparation - the Method for Teaching Data Science course.- Chapter 10. Data Science for Social Science.- Chapter 11. Conclusion.

Select Guide Rating