Free online courseIntroduction to Computational Thinking

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.