MTDS Course Schedule

• Reflection on the preliminary questionnaire
• Discussion: What is data science?
• Principles of data science teaching

• 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

• 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)

• Question Workshop: Exposure to the Question Database and a discussion about:

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

• 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)

• 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

• 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)

• 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)

• Data science skills (see Chapter ‎11): Storytelling and the rhetoric triangle

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

• Presentation of asynchronous task #7 on different machine learning algorithms (see Table 18.2 – Table ‎2, in Grading Policy and Submissions)
• Students’ presentations of their papers/reports/curricula on data science education – Part 1 (three presentations) (see Assignment #5, Table ‎18.1 – Table ‎1, in Grading Policy and submission)

• Presentation of asynchronous task #8 on mentoring data science projects in the high school (see Table 18.2 – Table ‎2, in Grading Policy and submissions)
• Students’ presentations of their papers/reports/curricula on data science education – Part 2 (four presentations) (see Assignment #5, Table ‎18.1 – Table 1, in Grading Policy and submissions)

• Course summary