MTDS Course Schedule

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:

Hazzan, O. and Mike, K. (2023). Guide to Teaching Data Science: An Interdisciplinary ApproachSpringer.
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 21st 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   Project-based 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
  • Mid-semester 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 process-object 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 11th 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
  • Course summary