Computational Thinking Problem Solving Techniques (PDF)

Module 01: Computational Thinking Problem Solving Techniques (Real Life Examples) 1 © 2021 Nanyang Technological University, Singapore. All Rights Reserved. ...

Module 01: Computational Thinking Problem Solving Techniques (Real Life Examples) 1 © 2021 Nanyang Technological University, Singapore. All Rights Reserved.  Abstraction - Example 2 © 2021 Nanyang Technological University, Singapore. All Rights Reserved.  Algorithm - Example 3 © 2021 Nanyang Technological University, Singapore. All Rights Reserved.  Decomposition - Example 4 © 2021 Nanyang Technological University, Singapore. All Rights Reserved.  Pattern Recognition - Example 5 © 2021 Nanyang Technological University, Singapore. All Rights Reserved.  Abstraction in Arts Art, when viewed as a form of abstractions of real world, has been created since stone age Symbols have been used a form of communication between past and present 6 © 2021 Nanyang Technological University, Singapore. All Rights Reserved.  Official processes in legislative system with usages including Assist in maintaining consistency Reducing bias E.g., Process of creating a law: Algorithms Introducing Presenting Voting in for in Legal the bill in Parliament Debating Parliament President’s approval Settings 7 © 2021 Nanyang Technological University, Singapore. All Rights Reserved.  Prosecution Process viewed as an Algorithm 8 © 2021 Nanyang Technological University, Singapore. All Rights Reserved.  Decomposition – Piggy Bank Example How to count coins efficiently? Sort them first, then count © 2021 Nanyang Technological University, Singapore. All Rights Reserved. 9  Decomposition – Company Example Companies have departments and teams, with different functions and power 10 © 2021 Nanyang Technological University, Singapore. All Rights Reserved.  Pattern Recognition – Geology Example Geology: The study of structure, evolution, and dynamics of the Earth and its natural mineral and energy resources. Pangaea: A supercontinent made up of the current 7 continents. © 2021 Nanyang Technological University, Singapore. All Rights Reserved. 11  Abstraction in English Language Arts (ELA) Abstraction involves the induction of ideas or the synthesis of particular facts into one general theory. To provide a book synopsis: sieve out the main plotline of the book; omit small details describing the appearance of characters © 2021 Nanyang Technological University, Singapore. All Rights Reserved.  Algorithms in ELA Traditional Poetry defined due to its regular rhythm, verse structure and rhyme scheme. https://sites.research.google/versebyverse/ © 2021 Nanyang Technological University, Singapore. All Rights Reserved. 13  Decomposition in ELA 14 © 2021 Nanyang Technological University, Singapore. All Rights Reserved.  Pattern Recognition in ELA Phonics is used to learn to pronounce new words by children Patterns and rules can be derived from spellings © 2021 Nanyang Technological University, Singapore. All Rights Reserved. 15  Module 01: Computational Thinking Problem Solving Techniques (Biology) 1 © 2021 Nanyang Technological University, Singapore. All Rights Reserved.  Computational Thinking Competencies (4 main) Computational: Involving the calculation of answers, amounts, results( e.g., calculations, order) Thinking: The activity of using your mind to consider something (e.g., reasoning, questioning) Competencies: Important skills that are needed to do a job (e.g., managerial competencies) Pattern Abstraction Algorithms Decomposition Recognition 2 © 2021 Nanyang Technological University, Singapore. All Rights Reserved.  Abstraction: Biology Abstraction: Identifying and utilizing the structure of concepts / main ideas Simplifies things Identifies what is important without worrying too much about the detail Allows us to manage the complexity of the context or content The abstraction process – deciding what details we need to highlight and what details we can ignore – underlies computational thinking. - Jeannette Wing 3 © 2021 Nanyang Technological University, Singapore. All Rights Reserved.  Bioinformatics Combines different fields of study, including computer sciences, molecular biology, biotechnology, statistics and engineering Large amount of data: Genomics, Proteomics Pseudocode: An informal description of the steps involved in executing a computer program, often written in something similar to plain [in designed language] 4 © 2021 Nanyang Technological University, Singapore. All Rights Reserved.  Human Genomes (Abstraction) Structure of cell: Incredibly crowded Incomprehensible for humans Question: How to simplify the representation of cells? How to make it readable? Answer: By abstraction: labelling, lettering, shaping, colouring, etc. © 2021 Nanyang Technological University, Singapore. All Rights Reserved. 5  Human Genomes (Abstraction) Formulating in pseudo level can enable us to understand concepts more clearly. Abstraction simplifies complex life phenomenon to something readable and understandable. © 2021 Nanyang Technological University, Singapore. All Rights Reserved. 6  Algorithms in Biology Algorithm is about following, identifying, using, and creating an ordered set of instructions ordering things ascending order (e.g., from 1 to 5, or from A B C to X Y Z) descending order (e.g., from 5 to 1, or from Z Y X to C B A) Allows us to order the complexity of the context or content 7 © 2021 Nanyang Technological University, Singapore. All Rights Reserved.  Algorithms Biology (overview) 8 © 2021 Nanyang Technological University, Singapore. All Rights Reserved.  Algorithms Biology (overview) 9 © 2021 Nanyang Technological University, Singapore. All Rights Reserved.  Decomposition is about: Breaking down data, processes or problems into smaller and more manageable components to solve a problem Decomposition Each subproblem can then be examined or in Biology solved individually, as they are simpler to work with Natural way to solve problems Also known as divide-and-conquer 10 © 2021 Nanyang Technological University, Singapore. All Rights Reserved.  Decomposition (divide and conquer) 11 © 2021 Nanyang Technological University, Singapore. All Rights Reserved.  Decomposition Solve complex problems If a complex problem is not decomposed, it is much harder to solve at once. Subproblems are usually easy to tackle Each subproblem can be solved by different parties of analysis Decomposition forces you to analyze your problem from different aspects 12 © 2021 Nanyang Technological University, Singapore. All Rights Reserved.  Pattern Recognition is about observing patterns, trends and regularities in data A pattern is a discernible regularity The elements of a pattern repeat in a predictable manner In computational thinking, a pattern is the spotted similarities and common differences between problems It involves finding the similarities or patterns among small, decomposed problems, which can help us solve complex problems more efficiently 13 © 2021 Nanyang Technological University, Singapore. All Rights Reserved.  Pattern Recognition in Biology 14 © 2021 Nanyang Technological University, Singapore. All Rights Reserved.  Pattern Recognition Patterns make problems simpler and easy to solve Problems are easier to solve when they share patterns, we can use the same problem-solving solution wherever the pattern exists The more patterns we can find, the easier and quicker our problem solving will be 15 © 2021 Nanyang Technological University, Singapore. All Rights Reserved.  CC0002 Navigating the Digital World Module 2: Quantitative Reasoning Techniques Testing Techniques © 2024 Nanyang Technological University, Singapore. All Rights Reserved.  Case Study Does the drug you take for headache work as claimed? How do you know for sure? Can it be a placebo instead? How do you test its efficacy? 2021 Nanyang Technological University, Singapore. All Rights Reserved. © 2024  Quantitative Reasoning Desired insights on the problem Suppose you take the A. Does the drug at all reduce your headache in reasonable time? drug now, and your B. Does the drug manage to work better (faster) than a placebo? headache goes away within the next hour. Steps to obtain the desired insights How to frame concrete numerical questions? How to identify tools and data for analysis? Does this mean the How to build models to analyse the data? drug is effective? How to analyse the results you obtain? (Write down what you think.) © 2024 Nanyang Technological University, Singapore. All Rights Reserved.  Identify Your Data What type of data is relevant? Binary: Did your headache subside? Single trial of drug: YES/NO Continuous: How long did it take to subside? Single trial of drug: 16 minutes How much data do you need? Is it sufficient to have a single data point? Single trial: 16 Is it required to have a million data points? Multiple trials: 16, 18, 24, 20, … Do you want a comparison? Which base case would you compare with? Drug trials: 16, 18, 24, 20, … Is it possible to get data for both the cases? Placebo trials: 28, 23, 18, 21, … © 2024 Nanyang Technological University, Singapore. All Rights Reserved.  Formulate Your Question Which case seems to be better? Simulated Experiments Will better in any one of the trials suffice? Drug trials: 16, 18, 24, 20, … Does it have to be better in all the trials? Placebo trials: 28, 23, 18, 21, … Is it fine if one is better on average? Number of trials = 30 in each case Is average behaviour sufficient? Compare the Means What if the drug seems better on average? Mean(Drug) : 18 minutes Do you know if the drug will always be better? Mean(Placebo) : 22 minutes How about being better most of the time? Mean is merely a single- Suddenly, things look not too obvious! © point statistic from our entire data distribution. © 2024 Nanyang Technological University, Singapore. All Rights Reserved.  What is Data Distribution? Representation: Line plot Simulated Experiments Plot data-points connected by lines Drug trials 30 16, 18, 24, 20, 18, 19, 15, 15, 18, 19, 25 20, 27, 12, 21, 20, 17, 16, 10, 18, 20, 21, 24, 14, 24, 16, 11, 18, 16, 19, 14. 20 Placebo trials 15 28, 23, 18, 21, 21, 24, 22, 18, 21, 24, 10 13, 25, 22, 19, 21, 25, 21, 20, 19, 19, 21, 23, 26, 28, 25, 19, 25, 24, 20, 25. 5 Number of trials = 30 in each case 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Drug Placebo © 2024 Nanyang Technological University, Singapore. All Rights Reserved.  What is Data Distribution? Representation: Histogram Simulated Experiments Count frequency across specific bins Drug trials 7 Drug 16, 18, 24, 20, 18, 19, 15, 15, 18, 19, Placebo 20, 27, 12, 21, 20, 17, 16, 10, 18, 20, 6 21, 24, 14, 24, 16, 11, 18, 16, 19, 14. 5 Placebo trials 4 28, 23, 18, 21, 21, 24, 22, 18, 21, 24, 3 13, 25, 22, 19, 21, 25, 21, 20, 19, 19, 21, 23, 26, 28, 25, 19, 25, 24, 20, 25. 2 1 Number of trials = 30 in each case 0 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 © 2024 Nanyang Technological University, Singapore. All Rights Reserved.  Case Study Are you paying as is expected to buy your new house? How do you know for sure? Can it be over/underpriced? How do you estimate the price? 2021 Nanyang Technological University, Singapore. All Rights Reserved. © 2024  Quantitative Reasoning Desired insights on the problem Suppose you find a A. Does the price of the house at all depend on these features? 1710 sq. ft. 5-year-old B. Is the quoted price reasonable good quality (7 of 10) given the features of the house? house at $208,500. Steps to obtain the desired insights How to frame concrete numerical questions? Does this mean you How to identify tools and data for analysis? landed a good deal? How to build models to analyse the data? How to analyse the results you obtain? (Write down what you think.) © 2024 Nanyang Technological University, Singapore. All Rights Reserved.  Identify Your Data What type of data is relevant? Binary : Is this a good deal or a bad deal? Single house deal : YES/NO Continuous : What is the final sale price? Single house deal : $208,500 How much data do you need? Is it sufficient to have a single data point? Single house : 208500 Is it required to have a million data points? Multiple houses : 208500, 181500, 223500, 140000, … Do you want an estimation? Which features do you need for a house? Features : Age, area, quality Is it possible to get data for all features? Response : Price (to be estimated) © 2024 Nanyang Technological University, Singapore. All Rights Reserved.  Formulate Your Question What if we estimate price naively? Surveyed Houses Generic estimate = Mean (Price) = 182517 Features : Age, area, quality How wrong can this estimate be in general? Response : Price (to be estimated) What is your confidence on this estimate? Number of data samples = 500 houses Age Area Quality Price Which feature is the strongest? 5 1710 7 208500 Does age determine the price of a house? 31 1262 6 181500 7 1786 7 223500 Or does area have more effect on the price? 91 1717 7 140000 Or is it quality that affects the price most? 8 2198 8 250000 16 1362 5 143000 3 1694 8 307000 Suddenly, things look more complicated! ☺ 36 2090 7 200000 77 1774 7 129900 69 1077 5 118000 © 2024 Nanyang Technological University, Singapore. All Rights Reserved.  10 20 25 30 35 40 15 0 5