CC0002: Navigating The Digital World PDF

CC0002: Navigating The Digital World MODULE 1: Computational Thinking and Problem Solving Computational Thinking Competencies: Computational: involving calculations, amounts, and results. Thinking: the activity of using your mind to consider something Competencies: important skills t...

CC0002: Navigating The Digital World MODULE 1: Computational Thinking and Problem Solving Computational Thinking Competencies: Computational: involving calculations, amounts, and results. Thinking: the activity of using your mind to consider something Competencies: important skills that are needed to do a job (like managerial competencies) Computational Thinking Methods ORDER: Decomposition → Abstraction → Pattern Recognition → Algorithms 1. Decomposition: the process of breaking down data, processes, or problems into smaller and more manageable components to solve a problem Each subproblem (easier to tackle) is then analyzed and solved by different parties, thus forcing us to analyze the problem from different aspects. → also known as the divide-and-conquer strategy to synthesize the final solution. → actually a natural way to solve problems. 2. Abstraction: identifying and utilizing the structure of concepts or main ideas (deciding what details we need to highlight and what details we can ignore) Its job is to simplify things such that it can manage the complexity of the context of content 3. Pattern Recognition: observing patterns, trends, and regularities in data A pattern is a discernible regularity (the elements repeat in a predictable manner) In CT, a pattern is the spotted similarities AND differences between problems. It involves finding the similarities or patterns among small, decomposed problems, which can help us solve complex problems more efficiently → patterns make problems simpler to solve → we can use the same problem-solving solution wherever the pattern exists → the more patterns we can find, the easier and quicker problem-solving will be 4. Algorithm: about following, identifying, using, and creating an ordered set of instruction Ordering things - In ascending order (1 to 5 or A to Z) - In descending order (5 to 1 or Z to A) Allows us to order the complexity of the content and context Real-life based examples/ scenarios 1. Biology: Decomposition: Biological decomposers (fungi, bacteria)  Abstraction: Human Genomes modeling (the structure of a human cell is crowded and incomprehensible for humans, so, we label them by letters, shapes, and coloring in simpler models understandable to men. Pattern recognition: Divide the biological scope according to their similarities. (gene finding, biomarkers, protein synthesis) 2. English language Arts: Abstraction: making a book synopsis Algorithms: traditional poetry structure (verse and rhyme) Decomposition: essay outline Pattern recognition: phonics and spelling 3. Computer Science Manifestations: Decomposition: Functions and Factorials Abstraction: Pseudocode Pattern Recognition: Machine learning, AI, probability and Statistics Algorithms: IF ELSE, ALgorithm effieciency 4. Singapore MRT map: Abstraction (take information only about the stations instead of the whole detailed map of SG) 5. Instructions to build a chair : Algorithm (can also be like recipes) 6. Candy crush game: Pattern recognition (matching same items together) 7. Abstraction in Art: created since the stone age. Symbols have been used a form of communication between past and present 8. Algorithms in Legal Settings: Official processes in legislative system with usages including 1). Assist in maintaining consistency and 2). Reducing bias The process of making a law: Introducing Bill in parliament → Debating → Voting in Parliament → Presenting for President’s approval 9. Decomposition in Piggy Bank Counting: Instead of counting each coin directly, we will sort them based on their values, then count (by multiplying amount * value) 10. Decomposition in Company: companies have departments, divisions, and teams, with different functions and power 11. Pattern Recognition in Geology: The study of structure, evolution, and dynamics of the Earth and its natural mineral and energy resources (Pangaea) Application of different CT methods in solving mazes https://drive.google.com/drive/folders/1PvCNgX-LCUUMh3izZUBvT5WbdoF8O8jf Advantages / Disadvantages of CT and its different methods MODULE 2: Quantitative Reasoning and Techniques Importance of QR in data analysis & Steps to obtain desired insights: 1. How to frame concrete numerical questions? 2. How to identify tools and data for analysis? 3. How to build models to analyze the data? 4. How to analyze the results obtained? Techniques or methods (Mean, Standard Deviation, Linear Regression, Correlation) to build a prediction model. 1. Mean: the “average” behavior of the data points (single point statistic from entire data distribution) 2. Standard Deviation: the average deviation of a data point from the mean of the distribution (a higher SD means a wider distribution) 3. Linear Regression: statistical method used to model the relationship between a dependent variable and one or more independent variables The goal is to find the best-fitting line that predicts the value of the dependent variable based on the independent variables. 4. Correlation: -1