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computational thinking

Track :

Computer Science

Lessons no : 32

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What will you learn in this course?
  • Develop problem-solving skills using abstraction, decomposition, and algorithm design for real-world applications in computing
  • Apply computational thinking frameworks to analyze complex problems and develop effective solutions
  • Utilize data analysis, coding, and algorithmic strategies to optimize computational processes and workflows
  • Explain core concepts of algorithms, data structures, and computational limits in modern computing systems
  • Design efficient algorithms by breaking down problems into manageable components and identifying key patterns
  • Implement computational solutions that address real-world challenges across various industries and domains
  • Evaluate the effectiveness of algorithms and computational methods in solving practical problems
  • Use computational thinking to innovate and improve existing software, hardware, and data systems
  • Communicate complex computational ideas clearly using appropriate technical language and visualizations
  • Integrate abstraction, decomposition, and algorithmic thinking to enhance problem-solving in technology-driven environments

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Lessons | 32
Introduction to Computational Thinking and Data Science | Optimization | 0 1 Knapsack Problem
00:11:30

Introduction to Computational Thinking and Data Science | Optimization | 0 1 Knapsack Problem
Introduction to Computational Thinking and Data Science | Optimization | Greedy Algorithms
00:18:04

Introduction to Computational Thinking and Data Science | Optimization | Greedy Algorithms
Introduction to Computational Thinking and Data Science | Decision Trees | Brute Force Algorithms
00:15:29

Introduction to Computational Thinking and Data Science | Decision Trees | Brute Force Algorithms
Introduction to Computational Thinking and Data Science | Decision Trees | Recursive Fibonacci
00:11:15

Introduction to Computational Thinking and Data Science | Decision Trees | Recursive Fibonacci
Introduction to Computational Thinking and Data Science | Decision Trees | Dynamic Programming
00:16:47

Introduction to Computational Thinking and Data Science | Decision Trees | Dynamic Programming
Introduction to Computational Thinking and Data Science | Graph Problems | Graphs
00:09:21

Introduction to Computational Thinking and Data Science | Graph Problems | Graphs
Introduction to Computational Thinking and Data Science | Graph Problems | Graph Class
00:12:38

Introduction to Computational Thinking and Data Science | Graph Problems | Graph Class
Introduction to Computational Thinking and Data Science | Graph Problems | Finding the Shortest Path
00:14:37

Introduction to Computational Thinking and Data Science | Graph Problems | Finding the Shortest Path
Introduction to Computational Thinking and Data Science | Stochastic Thinking | Stochastic Processes
00:09:00

Introduction to Computational Thinking and Data Science | Stochastic Thinking | Stochastic Processes
Introduction to Computational Thinking and Data Science | Stochastic Thinking | Probabilities
00:13:55

Introduction to Computational Thinking and Data Science | Stochastic Thinking | Probabilities
Introduction to Computational Thinking and Data Science | Stochastic Thinking | Random Walks
00:10:27

Introduction to Computational Thinking and Data Science | Stochastic Thinking | Random Walks
Introduction to Computational Thinking and Data Science | Random Walks | Location Field and Drunk
00:09:12

Introduction to Computational Thinking and Data Science | Random Walks | Location Field and Drunk
Introduction to Computational Thinking and Data Science | Random Walks | Simulating a Walk
00:12:11

Introduction to Computational Thinking and Data Science | Random Walks | Simulating a Walk
Introduction to Computational Thinking and Data Science | Inferential Statistics | Variation in Data
00:12:00

Introduction to Computational Thinking and Data Science | Inferential Statistics | Variation in Data
Introduction to Computational Thinking and Data Science | Inferential Statistics | Distributions
00:17:56

Introduction to Computational Thinking and Data Science | Inferential Statistics | Distributions
Intro to Computational Thinking and Data Science | Monte Carlo Simulations | Central Limit Theorem
00:10:47

Intro to Computational Thinking and Data Science | Monte Carlo Simulations | Central Limit Theorem
Intro to Computational Thinking and Data Science | | Monte Carlo Simulations Simulation to Find Pi
00:15:29

Intro to Computational Thinking and Data Science | | Monte Carlo Simulations Simulation to Find Pi
Intro to Computational Thinking and Data Science | Sampling and Standard Error | Data Sampling
00:11:46

Intro to Computational Thinking and Data Science | Sampling and Standard Error | Data Sampling
Introduction to Computational Thinking and Data Science | Sampling and Standard Error | Sample Sizes
00:08:48

Introduction to Computational Thinking and Data Science | Sampling and Standard Error | Sample Sizes
Intro to Computational Thinking and Data Science | Sampling and Standard Error | Standard Error
00:08:35

Intro to Computational Thinking and Data Science | Sampling and Standard Error | Standard Error
Intro to Computational Thinking and Data Science | Independent Random Samples
00:05:16

Intro to Computational Thinking and Data Science | Independent Random Samples
Intro to Computational Thinking and Data Science | Experimental Data | Fitting a Model to Data
00:10:25

Intro to Computational Thinking and Data Science | Experimental Data | Fitting a Model to Data
Introduction to Computational Thinking and Data Science | Experimental Data | Goodness of Fit
00:09:27

Introduction to Computational Thinking and Data Science | Experimental Data | Goodness of Fit
Introduction to Computational Thinking and Data Science | Experimental Data | Fits for Datasets
00:08:14

Introduction to Computational Thinking and Data Science | Experimental Data | Fits for Datasets
Intro to Computational Thinking Data Science | Experimental Data | Model Predictions and Overfitting
00:07:55

Intro to Computational Thinking Data Science | Experimental Data | Model Predictions and Overfitting
Introduction to Computational Thinking and Data Science | Experimental Data | Cross Validation
00:16:26

Introduction to Computational Thinking and Data Science | Experimental Data | Cross Validation
Intro to Computational Thinking and Data Science | Machine Learning | What is Machine Learning
00:14:28

Intro to Computational Thinking and Data Science | Machine Learning | What is Machine Learning
Introduction to Computational Thinking and Data Science | Machine Learning | K Nearest Neighbors
00:18:02

Introduction to Computational Thinking and Data Science | Machine Learning | K Nearest Neighbors
Introduction to Computational Thinking and Data Science | Machine Learning | Clustering
00:12:26

Introduction to Computational Thinking and Data Science | Machine Learning | Clustering
Intro to Comp Thinking and Data Science | Statistical Fallacies | Lies Damned Lies and Statistics
00:12:09

Intro to Comp Thinking and Data Science | Statistical Fallacies | Lies Damned Lies and Statistics
Introduction to Computational Thinking and Data Science | Statistical Fallacies | Texas Sharpshooter
00:16:08

Introduction to Computational Thinking and Data Science | Statistical Fallacies | Texas Sharpshooter
Intro to Computational Thinking and Data Science | Statistical Fallacies | Correlation and Causation
00:14:23

Intro to Computational Thinking and Data Science | Statistical Fallacies | Correlation and Causation


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Computational Thinking teaches the use of abstraction and decomposition when solving complex problems; it presents a framework for understanding algorithms; and it describes essential concepts in dealing with data and code and in expressing the limits of modern computing machinery .