Blockly Lists & Iteration CT @ VT Things we are seeing Using - PowerPoint PPT Presentation

Aug 08, 2023 •169 likes •275 views

Introduction to Computational Thinking Blockly Lists & Iteration CT @ VT Things we are seeing Using lists to represent a data stream Blockly blocks for iteration Using iteration to process a data stream (i.e., a list)

Introduction to Computational Thinking Blockly Lists & Iteration
CT @ VT Things we are seeing  Using lists to represent a data stream  Blockly blocks for iteration  Using iteration to process a data stream (i.e., a list)  Different patterns for processing a data stream  Uniform  Accumulate  Filter  Transform Spring 201 2015 Slide 2
CT @ VT Patterns  Uniform: perform the same processing on each element of the list  Accumulate: determine a property of the list  Filter: use a decision to select some elements of the list for processing  Transform: produce a new list by processing the elements of an existing list Spring 201 2015 Slide 3
CT @ VT Thinking about iteration middle start end Spring 201 2015 Slide 4
CT @ VT Accumulate pattern middle property property Spring 201 2015 Slide 5
CT @ VT Finding the total middle 10 7 15 9 14 2 4 32 34 10 7 2 14 4 15 9 Spring 201 2015 Slide 6
CT @ VT Finding the total beginning 10 7 15 9 14 2 4 0 end 10 7 15 9 14 2 4 61 Spring 201 2015 Slide 7
CT @ VT Filter pattern  Motivation: in big data only certain occurrences may be of interest  Big earthquakes  Temperatures above or below a certain threshold  A high or low crime rate  Mechanism: combine  iteration – to provide each element one at a time  decision - process each element that passes the test Spring 201 2015 Slide 8
CT @ VT Filter pattern Basic step A true Test A Process A false Spring 201 2015 Slide 9
CT @ VT Next steps today  Work on the assigned Blockly and list problems in the book using iteration and decision  Work as an individual  Seek help from and provide help to your cohort  Go as far and as fast as you can remembering to keep everyone in your cohort on board  Cohort feedback/reports  Start looking at data streams (7.4-7.6 in the book) Spring 201 2015 Slide 10

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