book

Applied Computational Thinking with Python

Name: Applied Computational Thinking with Python
ISBN: 9781839219436

by Sofía De Jesús, Dayrene Martinez

November 2020

Intermediate to advanced

420 pages

8h 50m

English

Packt Publishing

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Applied Computational Thinking with Python
Why subscribe?ContributorsAbout the authorsAbout the reviewerPackt is searching for authors like you
Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesDownload the color imagesConventions usedGet in touchReviews
Section 1: Introduction to Computational Thinking
Chapter 1: Fundamentals of Computer Science
Technical requirementsIntroduction to computer scienceLearning about computers and the binary systemConverting from binary to base-10Converting from base-10 to binaryUnderstanding theoretical computer scienceAlgorithmsCoding theoryData compressionError correctionCryptographyComputational biologyData structuresInformation theoryAutomata theoryFormal language theorySymbolic computationComputational geometryComputational number theoryLearning about a system's softwareOperating systemsApplication softwareUnderstanding computingArchitectureInstruction Set Architecture (ISA)Programming languagesLearning about data types and structuresData typesData structuresSummary
Chapter 2: Elements of Computational Thinking
Technical requirementsUnderstanding computational thinkingProblem 1 - Conditions Decomposing problemsRecognizing patternsProblem 2 - Mathematical algorithms and generalizationGeneralizing patternsDesigning algorithmsAdditional problemsProblem 2 - Children's soccer partyProblem 3 - Savings and interestSummary
Chapter 3: Understanding Algorithms and Algorithmic Thinking
Technical requirementsDefining algorithms in depthAlgorithms should be clear and unambiguousAlgorithms should have inputs and outputs that are well definedAlgorithms should have finitenessAlgorithms have to be feasibleAlgorithms are language-independentDesigning algorithmsProblem 1 – An office lunchOffice lunch mathematical algorithmOffice lunch Python algorithmProblem 2 – A catering companyAnalyzing algorithmsAlgorithm analysis 1 – States and capitalsAlgorithm analysis 2 – Terminating or not terminating?Summary
Chapter 4: Understanding Logical Reasoning
Technical requirementsUnderstanding the importance of logical reasoningApplying inductive reasoningSolving an inductive reasoning sample problemApplying deductive reasoningLearning about conditional statementsUnderstanding nested statementsUsing Boolean logic and operatorsThe and operatorThe or operatorThe not operatorIdentifying logic errors Summary
Chapter 5: Exploring Problem Analysis
Technical requirementsUnderstanding the problem definitionsProblem 5A – Building an online storeMaking assumptionsThings to considerBuilding a dictionaryLearning to decompose problemsConverting the flowchart into an algorithmBuilding a dictionary and giving inputsMaking changes to the costAdding personalizationAnalyzing problemsProblem 5B – Analyzing a simple game problemSummary
Chapter 6: Designing Solutions and Solution Processes
Technical requirementsDesigning solutionsProblem 1 - A marketing surveyDiagramming solutionsCreating solutionsProblem 2 - Pizza orderProblem 3 - Delays and PythonSummary
Chapter 7: Identifying Challenges within Solutions
Technical requirementsIdentifying errors in algorithm designSyntax errorsUsing colonsUsing nested parentheses and bracketsOther syntax errorsErrors in logic Debugging algorithms Comparing solutionsProblem 1 - Printing even numbersAlgorithm solution 1 - Printing even numbersAlgorithm solution 2 - Printing even numbersRefining and redefining solutionsSummary

Section 2:Applying Python and Computational Thinking
Chapter 8: Introduction to Python
Technical requirementsIntroducing PythonMathematical built-in functionsWorking with dictionaries and listsDefining and using dictionariesDefining and using listsUsing variables and functionsVariables in PythonCombining variablesWorking with functionsLearning about files, data, and iterationHandling files in PythonData in PythonUsing iteration in algorithmsUsing object-oriented programmingProblem 1 - Creating a book libraryProblem 2 - Organizing informationProblem 3 - Loops and mathUsing inheritanceSummary
Chapter 9: Understanding Input and Output to Design a Solution Algorithm
Technical requirementsDefining input and outputUnderstanding input and output in computational thinkingProblem 1 – Building a Caesar cipherProblem 2 – Finding maximumsProblem 3 – Building a guessing gameSummary
Chapter 10: Control Flow
Technical requirementsDefining control flow and its toolsUsing if, for, and range() and other control flow statementsUsing nested if statementsUsing for loops and rangeUsing other loops and conditionalsRevisiting functionsSummary
Chapter 11: Using Computational Thinking and Python in Simple Challenges
Technical requirementsDefining the problem and PythonDecomposing the problem and using Python functionalitiesGeneralizing the problem and planning Python algorithms Designing and testing the algorithmSummary
Section 3:Data Processing, Analysis, and Applications Using Computational Thinking and Python
Chapter 12: Using Python in Experimental and Data Analysis Problems
Technical requirementsDefining experimental dataUsing data libraries in PythonInstalling librariesUsing NumPy and pandasUsing MatplotlibUnderstanding data analysis with PythonUsing additional libraries for plotting and analysisUsing the Seaborn libraryUsing the SciPy libraryUsing the Scikit-Learn librarySummary
Chapter 13: Using Classification and Clusters
Technical requirementsData training and testingClassifying data exampleUsing the Scikit-Learn libraryDefining optimization modelsThe binary cross-entropy modelThe Adam optimization algorithmThe gradient descent modelThe confusion matrix modelImplementing data clusteringUsing the BIRCH algorithmUsing the K-means clustering algorithmSummary
Chapter 14: Using Computational Thinking and Python in Statistical Analysis
Technical requirementsDefining the problem and Python data selectionDefining pandasDetermining when to use pandasWorking with pandas seriesWorking with pandas DataFramesPreprocessing dataData cleaningWorking with missing dataWorking with noisy dataTransforming dataReducing dataProcessing, analyzing, and summarizing data using visualizationsProcessing dataAnalyzing and summarizing dataUsing data visualizationSummary
Chapter 15: Applied Computational Thinking Problems
Technical requirementsProblem 1 – Using Python to analyze historical speechesProblem 2 – Using Python to write storiesDefining, decomposing, and planning a storyProblem 3 – Using Python to calculate text readabilityProblem 4 – Using Python to find most efficient routeDefining the problem (TSP)Recognizing the pattern (TSP)Generalizing (TSP)Designing the algorithm (TSP)Problem 5 – Using Python for cryptographyDefining the problem (cryptography)Recognizing the pattern (cryptography)Generalizing (cryptography)Designing the algorithm (cryptography)Problem 6 – Using Python in cybersecurityProblem 7 – Using Python to create a chatbotSummary
Chapter 16: Advanced Applied Computational Thinking Problems
Technical requirementsProblem 1 – Using Python to create tessellationsProblem 2 – Using Python in biological data analysisProblem 3 – Using Python to analyze data for specific populationsDefining the specific problem to analyze and identify the populationProblem 4 – Using Python to create models of housing dataDefining the problemAlgorithm and visual representations of dataProblem 5 – Using Python to create electric field linesProblem 6 – Using Python to analyze genetic dataProblem 7 – Using Python to analyze stocksProblem 8 – Using Python to create a convolutional neural network (CNN)Summary
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Content preview from Applied Computational Thinking with Python

Chapter 12: Using Python in Experimental and Data Analysis Problems

In this chapter, we will take a look at how Python can help us understand and analyze data using algorithms and libraries created specifically for data analysis and data science. We will first go through experimental data and then move on to algorithms that use two main libraries: NumPy and pandas.

In this chapter, we will cover the following topics:

Defining experimental data
Using data libraries in Python
Understanding data analysis with Python
Using additional libraries for plotting and analysis

By the end of this chapter, you will be able to define types of experiments, data gathering, and how computational thinking helps when designing models and solutions. You will ...

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Applied Computational Thinking with Python

by Sofía De Jesús, Dayrene Martinez

Chapter 12: Using Python in Experimental and Data Analysis Problems

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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