Course Content
المحتوى
0/34
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
11:30
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
11:30
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
11:30
Introduction to Computational Thinking and Data Science | Stochastic Thinking | Probabilities
11:30
Introduction to Computational Thinking and Data Science | Stochastic Thinking | Random Walks
11:30
Introduction to Computational Thinking and Data Science | Random Walks | Location Field and Drunk
11:30
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
11:30
Introduction to Computational Thinking and Data Science | Inferential Statistics | Distributions
11:30
Intro to Computational Thinking and Data Science | Monte Carlo Simulations | Central Limit Theorem
11:30
Intro to Computational Thinking and Data Science | Monte Carlo Simulations | Simulation to Find Pi
11:30
Intro to Computational Thinking and Data Science | Sampling and Standard Error | Data Sampling
11:30
Introduction to Computational Thinking and Data Science | Sampling and Standard Error | Sample Sizes
11:30
Intro to Computational Thinking and Data Science | Sampling and Standard Error | Standard Error
11:30
Intro to Computational Thinking and Data Science | Independent Random Samples
11:30
Intro to Computational Thinking and Data Science | Experimental Data | Fitting a Model to Data
11:30
Introduction to Computational Thinking and Data Science | Experimental Data | Goodness of Fit
11:30
Introduction to Computational Thinking and Data Science | Experimental Data | Fits for Datasets
11:30
Intro to Computational Thinking Data Science | Experimental Data | Model Predictions and Overfitting
11:30
Introduction to Computational Thinking and Data Science | Experimental Data | Cross Validation
11:30
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
11:30
Intro to Comp Thinking and Data Science | Statistical Fallacies | Lies Damned Lies and Statistics
11:30
Introduction to Computational Thinking and Data Science | Statistical Fallacies | Texas Sharpshooter
11:30
Intro to Computational Thinking and Data Science | Statistical Fallacies | Correlation and Causation
11:30
Final Exam - Mastering Computational Thinking
Mastering Computational Thinking

Learn about the location field in random walks through the analogy of a drunken wanderer.

Previous
Next
0% Complete