Friday, June 8, 2018 Academic Innovation Studio, Dwinelle 117 (Level D) In this new one-day workshop, graduate students will learn about the pedagogy of the Data Science Education Program, where UC Berkeley is leading in curricular innovation in this exciting area. This workshop will cover some of the computational and statistical concepts that students learn in the Foundations of Data Science class (Data 8) and Principles and Techniques of Data Science (Data 100), as well as an overview of the Connector courses and Modules. It will also provide experience using a cloud based computing environment. Graduate students can use these skills to improve teaching, to imagine new types of teaching, and learn what resources are available. Open to UC Berkeley graduate students. No previous programming or statistics experience needed. Workshop Topics Overview of Data 8: inference, computational thinking, and Python Introduction to Data 100: Principles and Techniques of Data Science. Data-Centered Computing Environment: tables in the cloud, computational documents Learn to create and teach assignments within the Jupyter notebook system Sample lectures and labs from applied courses (Humanities, Social Sciences, Natural Sciences, Engineering) Understand the infrastructure behind the pedagogy platform Incorporate scientific reproducibility practices into teaching Learn about the work and student teams in the Data Science Education program Register for the workshop. Contact Eric Van Dusen with questions.
Friday, June 8, 2018 Academic Innovation Studio, Dwinelle 117 (Level D) In this new one-day workshop, graduate students will learn about the pedagogy of the Data Science Education Program, where UC Berkeley is leading in curricular innovation in this exciting area. This workshop will cover some of the computational and statistical concepts that students learn in the Foundations of Data Science class (Data 8) and Principles and Techniques of Data Science (Data 100), as well as an overview of the Connector courses and Modules. It will also provide experience using a cloud based computing environment. Graduate students can use these skills to improve teaching, to imagine new types of teaching, and learn what resources are available. Open to UC Berkeley graduate students. No previous programming or statistics experience needed. Workshop Topics Overview of Data 8: inference, computational thinking, and Python Introduction to Data 100: Principles and Techniques of Data Science. Data-Centered Computing Environment: tables in the cloud, computational documents Learn to create and teach assignments within the Jupyter notebook system Sample lectures and labs from applied courses (Humanities, Social Sciences, Natural Sciences, Engineering) Understand the infrastructure behind the pedagogy platform Incorporate scientific reproducibility practices into teaching Learn about the work and student teams in the Data Science Education program Register for the workshop. Contact Eric Van Dusen with questions.