Free online courseIntroduction to Computational Thinking
Duration of the online course: 12 hours and 9 minutes
New course
Master computational thinking and data science with MIT's free online course. Dive into optimization problems, stochastic thinking, Monte Carlo simulations, machine learning, and more.
Course Description
Welcome to "Introduction to Computational Thinking," a comprehensive course that provides an enriching exploration into the world of Artificial Intelligence. With a total duration of 12 hours and 9 minutes, this course is a detailed journey through crucial concepts and methodologies that form the foundation of computational thinking and data science.
In this course, you will embark on a quest to demystify complex topics, starting with an introduction to optimization problems. Optimization forms the essence of computational strategy, where you'll learn to develop solutions that maximize or minimize specific objectives within given constraints. You’ll learn how essential this skill is when tackling real-world problems and computational tasks.
Following the optimization problems, the course delves into graph-theoretic models, providing insights into how nodes and connections form the basis of networks and their applications. This foundational knowledge will allow you to understand social networks, logistics, and even how information spreads through systems.
Moving forward, the course introduces you to stochastic thinking and random walks. These subjects will expose you to unpredictability and random events in systems, setting the stage for exploring the nature of randomness in computational problems and how Monte Carlo simulations can be used to model and predict outcomes in systems where random variables play a significant role.
With a solid grasp of stochastic techniques, you'll then study confidence intervals, learning how to quantify the uncertainty in your data and make informed predictions, a skill crucial for data scientists dealing with real-world data analysis.
The course also sheds light on sampling and standard error, facilitating your understanding of how to collect data samples and measure the precision of your estimates, leading to more accurate and reliable experimental outcomes.
Understanding experimental data is another cornerstone of computational thinking. This section is divided into two comprehensive parts, ensuring that you develop a robust ability to interpret, analyze, and draw conclusions from experimental datasets.
Diving into the realm of Artificial Intelligence, the course introduces you to machine learning. You will explore the principles behind machine learning algorithms and how they enable computers to learn from data and improve over time.
Next, clustering and classification will be key focus areas. You'll see how clustering algorithms can group similar data points, while classification techniques help assign data points to predefined categories. Both are pivotal in making sense of large datasets and uncovering hidden patterns.
Furthermore, the course touches on classification and statistical missteps, often termed statistical sins, and how to avoid them. This crucial knowledge will help you maintain the integrity and robustness of your data analyses.
Finally, the course wraps up by revisiting important concepts, ensuring all the crucial elements of computational thinking and data science are well understood and consolidated.
Embark on this journey through computational thinking and equip yourself with the knowledge and skills to tackle a myriad of data-driven problems in the field of Artificial Intelligence and beyond.
Conteúdo do Curso
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Video class: 1. Introduction, Optimization Problems (MIT 6.0002 Intro to Computational Thinking and Data Science)
0h40m
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Exercise: _What is the goal of the problem sets in 60002?
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Video class: 2. Optimization Problems
0h48m
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Exercise: _What is the advantage of using a greedy algorithm?
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Video class: 3. Graph-theoretic Models
0h50m
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Exercise: _What is the purpose of MIT OpenCourseWare?
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Video class: 4. Stochastic Thinking
0h49m
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Exercise: _What is the reason why MIT OpenCourseWare asks for support from its users?
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Video class: 5. Random Walks
0h49m
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Exercise: _What is the purpose of defining the "Location" abstraction in the simulation of the drunk's walk?
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Video class: 6. Monte Carlo Simulation
0h50m
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Exercise: _Who invented the concept of Monte Carlo simulation?
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Video class: 7. Confidence Intervals
0h50m
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Exercise: _What is the purpose of the weights keyword argument in the pylab.hist function?
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Video class: 8. Sampling and Standard Error
0h46m
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Exercise: _What is the key idea behind simple random sampling?
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Video class: 9. Understanding Experimental Data
0h47m
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Exercise: _What is the purpose of MIT OpenCourseWare?
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Video class: 10. Understanding Experimental Data (cont.)
0h50m
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Exercise: _What is the objective function used in linear regression to measure the goodness of fit between observed and predicted data?
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Video class: 11. Introduction to Machine Learning
0h51m
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Exercise: _What is the purpose of clustering in machine learning?
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Video class: 12. Clustering
0h50m
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Exercise: _What is clustering in machine learning?
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Video class: 13. Classification
0h49m
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Video class: 14. Classification and Statistical Sins
0h49m
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Exercise: _What does the speaker suggest students do with the code provided for the final exam?
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Video class: 15. Statistical Sins and Wrap Up
0h44m
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Exercise: _What is the statistical sin being committed when people draw a conclusion by finding two points that are consistent with something they believe?
This free course includes:
12 hours and 9 minutes of online video course
Exercises to train your knowledge
Certificate of course completion
100% free, from content to certificate
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