Back to the Methods of Teaching Data Science course
Back to Technion Data Science Education website
The following table presents the course schedule.
Numbers of sections/chapters refer to our book:
Detailed descriptions of each lesson are linked to their description without the reference to book’s sections/chapters.
Lesson# 
Content 
Lesson 1 

Lesson 2 
 Presentation of the asynchronous task (see Table 18.2 – Table 2, in Grading Policy and Submissions)
 Data science and 21^{st} century skills Learning environments for data science
 Interdisciplinarity, in general, and the interdisciplinarity of data science, in particular

Lesson 3 
 Continuation of the discussion about the interdisciplinarity of data science:
o The pedagogical challenges that the interdisciplinarity of data science presents (Chapter 6)
o Projectbased learning (PBL, see Section 12.3.2.4)
Presentation of the asynchronous task that deals with the interdisciplinarity of computer science (see Table 18.2 – Table 2, in Grading Policy and Submissions)

Lesson 4 
o Educational entrepreneurship and the many opportunities open to entrepreneur teachers who teach new topics such as data science
o Data science teacher community and the role of teachers in the promotion of a learning community

Lesson 5 
 The Israeli high school data science curriculum (see Chapter 7)
 The Israeli high school data science program development process (see Chapter 9):
o Data science ideas highlighted in the program
o Mentoring the development of the final project as part of the program (30 out of 90 hours)

Lesson 6 
 Data science thinking (see Chapter 3)
 Presentation of the asynchronous task in which students were asked to prepare a presentation on computational thinking and statistical thinking (see Table 18.2 – Table 2, in Grading Policy and Submissions)
 Algorithms and data in data science and their relationships to computational thinking and statistical thinking
 Midsemester questionnaire

Lesson 7 
 Presentation of asynchronous task #5 on the history of data science (see Table 18.2 – Table 2, in Grading Policy and Submissions and Exercise 2.15 – Overview of the history of data science)
 The processobject conception of the KNN algorithm (see Section 3.2.3.1)
 Python programming
 White box and black box understandings (see Chapter 13)

Lesson 8 
 Presentation of asynchronous task #6 on ethics (see Table 18.2 – Table 2, in Grading Policy and Submissions)
 Continuation of Lesson 6 on data science thinking (see Chapter 3)
 The pedagogical chasm in data science education (see Chapter 9)
 The Israeli high school data science curriculum for 11^{th} grade AP computer science pupils (see Chapter 7)

Lesson 9 

Lesson 10 
 Panel of computer science high school teachers who teach the high school data science program

Lesson 11 

Lesson 12 

Lesson 13 
