Computational Thinking and Artificial Intelligence for STEM Majors

Along with reading, writing, and arithmetic, computational thinking is a skill that everyone living in the AI era must possess. Computational thinking can be simply defined as thinking like a programmer or a computer scientist. This course faithfully reproduces an actual university semester-long course and covers essential content for people encountering programming for the first time or beginners planning to major in computer science. Through this course, people without a background in computer science or with a humanities background can also develop the ability to communicate easily with professional programmers. Even in an era when learning prompts for AI vibe coding is necessary, this course helps you develop the ability to give AI more accurate and effective instructions through computational thinking.

It improves logical thinking skills and cultivates the capacity to converge one's major with information and communication technology in the era of the Fourth Industrial Revolution.

Are you a non-major who doesn't know how to start learning C language? Did you learn C language as a major in school, but still can't write code? What's the point of studying C language for a long time? When you can't even create a single line of code properly... I'll pass on the know-how so you can program yourself. In this course, together with an experienced senior developer, you'll gradually learn C language grammar and application methods, and I'll guide you to a level where you can work on simple projects.

Big data era!! Statistics are essential for analyzing and utilizing data! Let's look at basic statistical common sense that can be used to extract and utilize insights on customers and markets.

Slowly study the C language, which includes the basics of program development that developers need to know.

Data-driven decision-making can minimize errors caused by subjective judgment and intuition, and will serve as an important basis for predicting various risks in advance and establishing various strategies.

We’ll guide non-majors and beginners to quickly earn the Big Data Analysis Engineer practical certification! Keep the theory light and the practical training solid. Even without complex background knowledge, you’ll focus on past exam questions and study only the key points that are sure to appear on the test.

Instead of explanations focused on fundamental theories about 텍스톰(TEXTOM), we enable you to develop text mining and big data analysis capabilities through practical hands-on exercises.

✅ Out of the entire series (1) ~ (7), (4) data structure, algorithm, object-oriented, programming language ✅ CS101 series lectures - From basic computer theory to the latest practical examples - Provides a wide range of basic knowledge

We are now in an era where data speaks louder than intuition. Learn data analysis easily with ChatGPT!

Let's cultivate professional expertise in programming, algorithms, and statistical analysis!

It helps train the "constructive thinking" needed to frame problems.

This course is a hands-on program for practicing the fundamentals of machine learning mathematics and statistics using Python. While machine learning and deep learning require programming skills, a mathematical background is necessary to understand the underlying principles. Through this course, you can step-by-step master the foundational mathematics required for artificial intelligence.

--- Learning a technology means reclaiming the choices in your own life --- You may have connected an ESP32 development board and sensors before, but the process of developing a jumper-wire experiment into a complete device can feel daunting. There is a great deal of information online about circuit design, PCB fabrication, sensor control, and BLE and Wi-Fi communication. However, connecting this scattered information and completing a board that actually works requires a great deal of trial and error. In this course, you will design and build an ESP32-based sensor board yourself. You will configure the power and firmware download circuits, connect temperature and humidity and light sensors, design a PCB with KiCad, and order it for fabrication. Once the board is complete, you will upload firmware to it and transmit sensor data externally via BLE and Wi-Fi. By the end of the course, you will be able to go beyond simply assembling development boards and sensor modules: you will understand and be able to modify circuits and PCBs to suit your own purposes. The project completed in the course can be reused as a starting point for new projects such as environmental monitoring devices, smart home devices, educational kits, and IoT prototypes. Even in an era when generative AI can rapidly create digital content, physical information such as temperature, humidity, light, and movement in the real world can only be obtained through sensors and hardware. The ability to collect data with a device you built yourself and connect it to a network gives you the power to turn ideas into real-world systems. This course will be helpful for: * Those who have used Arduino or ESP32 development boards but are new to PCB design * Those who want to turn a sensor project made with jumper wires into a single complete board * Those who want to learn KiCad by following an actual ESP32 project * Those who want to build a device that collects sensor data and transmits it via BLE and Wi-Fi * Makers and educators developing environmental monitoring, smart home, or educational hardware * Aspiring entrepreneurs preparing their own IoT products or prototypes * Developers and content creators who want to analyze or process data collected from the real world or use it with AI models Move from being someone who uses development boards to someone who designs their own boards. This course does not merely teach you how to use sensors. It is a course where you experience the entire process of turning an idea into a circuit, turning the circuit into a PCB, and connecting the completed hardware to a network.

This is a Python basics course for starters who want to grow as data analysts. - Those who want to do data analysis but don't know Python - Those who want to learn machine learning with Python - Those preparing for the Big Data Analytics Engineer practical exam - Those who want to develop machine learning-based applications with Python

Even if you know nothing about coding, you can create your own scheduling app in just 4 weeks. Simply tell the AI, "Make an app like this," and paste the code you receive into Swift Playgrounds. Over 12 sessions—3 times a week, 30 minutes a day—you will complete a real app featuring adding/deleting/editing schedules, categories and notifications, filters and search, and even a calendar view. Don't worry if you run into errors; just ask the AI, and it will fix them for you. Start the challenge now to learn how to collaborate with AI, rather than just studying coding!

When learning Swift, the iOS programming language, it can be confusing when multiple concepts appear all at once. That's why I created this course with the principle that "when understanding a new concept, only previously learned concepts should be used." So this course is meticulously designed with a sequential structure between concepts, where only one new concept appears at each stage, and no other unfamiliar concepts appear at all. One concept per minute, 180 concepts total. From the basics of variables and constants to understanding memory structure from a CS perspective, and even the latest syntax for asynchronous processing. In 3 hours, you'll learn 180 core concepts in the designed order.

This is an online lecture that systematically organizes pathfinding and spatial partitioning algorithms used in game production.

"Beyond Data Analysis: Mastering Predictive Modeling with 5 Real-World Projects (45 Lectures Total)" Have you finished learning data analysis but feel stuck when it's time to actually build a model? Beyond simply learning how to call libraries, this course will help you fully master the inner workings of algorithms and optimal model validation strategies—ranging from Titanic survival prediction to spam text classification. Systematically conquer projects that wield the most power in the industry, from linear models to the latest ensemble algorithms and the basics of Natural Language Processing (NLP). Step into the world of AI modeling and start predicting the future based on analyzed data.

This course is for those who want to learn maker coding through entry block coding and learn the basics of coding. Even non-majors! Even students who are new to coding! You can learn the basic theories of coding and the basic usage of parts/sensors to engage in various maker activities.