Course Content
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Introduction to Computational Thinking and Data Science | Optimization | 0-1 Knapsack Problem
11:30
Introduction to Computational Thinking and Data Science | Optimization | 0-1 Knapsack Problem
11:30
Introduction to Computational Thinking and Data Science | Optimization | Greedy Algorithms
11:30
Introduction to Computational Thinking and Data Science | Decision Trees | Brute Force Algorithms
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Introduction to Computational Thinking and Data Science | Decision Trees | Recursive Fibonacci
11:30
Introduction to Computational Thinking and Data Science | Decision Trees | Dynamic Programming
11:30
Introduction to Computational Thinking and Data Science | Graph Problems | Graphs
11:30
Introduction to Computational Thinking and Data Science | Graph Problems | Graph Class
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Introduction to Computational Thinking and Data Science | Graph Problems | Finding the Shortest Path
11:30
Introduction to Computational Thinking and Data Science | Stochastic Thinking | Stochastic Processes
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Introduction to Computational Thinking and Data Science | Stochastic Thinking | Probabilities
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Introduction to Computational Thinking and Data Science | Stochastic Thinking | Random Walks
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Introduction to Computational Thinking and Data Science | Random Walks | Location Field and Drunk
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Introduction to Computational Thinking and Data Science | Random Walks | Simulating a Walk
11:30
Introduction to Computational Thinking and Data Science | Inferential Statistics | Variation in Data
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Introduction to Computational Thinking and Data Science | Inferential Statistics | Distributions
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Intro to Computational Thinking and Data Science | Monte Carlo Simulations | Central Limit Theorem
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Intro to Computational Thinking and Data Science | Monte Carlo Simulations | Simulation to Find Pi
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Intro to Computational Thinking and Data Science | Sampling and Standard Error | Data Sampling
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Introduction to Computational Thinking and Data Science | Sampling and Standard Error | Sample Sizes
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Intro to Computational Thinking and Data Science | Sampling and Standard Error | Standard Error
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Intro to Computational Thinking and Data Science | Independent Random Samples
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Intro to Computational Thinking and Data Science | Experimental Data | Fitting a Model to Data
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Introduction to Computational Thinking and Data Science | Experimental Data | Goodness of Fit
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Introduction to Computational Thinking and Data Science | Experimental Data | Fits for Datasets
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Intro to Computational Thinking Data Science | Experimental Data | Model Predictions and Overfitting
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Introduction to Computational Thinking and Data Science | Experimental Data | Cross Validation
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Intro to Computational Thinking and Data Science | Machine Learning | What is Machine Learning
11:30
Introduction to Computational Thinking and Data Science | Machine Learning | K Nearest Neighbors
11:30
Introduction to Computational Thinking and Data Science | Machine Learning | Clustering
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Intro to Comp Thinking and Data Science | Statistical Fallacies | Lies Damned Lies and Statistics
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Introduction to Computational Thinking and Data Science | Statistical Fallacies | Texas Sharpshooter
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Intro to Computational Thinking and Data Science | Statistical Fallacies | Correlation and Causation
11:30
Final Exam - Mastering Computational Thinking
Mastering Computational Thinking

This lesson demonstrates how Monte Carlo simulations can be used to estimate the value of Pi.

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