Lesson #2

Back to the Methods of Teaching Data Science course

Back to Technion Data Science Education website
  • Presentation of the asynchronous task (see Table ‎2 in Grading policy and submissions)
  • Discussion: Data science and the 21st century skills as part of the 4th industrial revolution.
  • Class activity: Learning environments for data science
    • The students worked in pairs on a presentation (later shared with the entire class) which asked to find learning environments for data science (for any level of learners) and to present each environment on one slide.
    • Colab, Orange and excel are among the environments the students located.
    • Reflection on their work experience in breakout rooms and to share their reflections via the chat function.
  • Interdisciplinarity:
    • Discussion about the concepts of multi-, inter-, and trans-disciplinarity.
      • The students are asked to organize the three concepts in a hierarchy according to the integration level of the disciplines represented in each concept and to share their answer via the chat function.
    • Discussion: Is data science multi-, inter-, or trans-disciplinary?
  • Feedback on the students’ first task (see Table 1 in Grading policy and submissions). The main topics they requested to learn in the course were:
    • The Israeli high school data science curriculum
    • Gaps in learners’ mathematical and statistical knowledge
    • Learners’ difficulties in learning data science
    • Experience of teachers who had already started teaching the new data science curriculum. Accordingly, we organized a panel of such teachers to speak in Lesson 10 of the course.