This course is designed to use only personal PCs and laptops. You can directly implement CI/CD automation processes by applying GitOps methodology in a k8s environment without using cost-incurring cloud environments like AWS or GCP.

No more 'Question - Answer - Read-through' explanations! We explain all the knowledge related to how to solve AWS SAA certification exam questions!

This course will teach you how to compose in Jetpack. You will learn the core ideas of compose, how to manage state using compose, and how to compose screens.

This course will teach you how to use authentication, database, storage, and FCM provided by Google Firebase. In particular, FCM is a core service of Firebase used by most apps for server push.

This course will teach you about networking, data persistence, and location utilization, which are essential for Android apps. These three features are implemented so frequently that it is hard to find an app that does not utilize them.

This course will teach you how Android components work and how to use them. The core components of Android apps are components. If you develop apps without a proper understanding of components, there will be more than one or two areas where you will get stuck.

This course will teach you the Jetpack library's views for creating various screens for real-world apps. The screens of real-world apps are diverse and complex, so they cannot be developed using only platform APIs.

This course covers the basics of Android app development. No matter what app you develop or what features you implement, you cannot develop without understanding the content of this course.

Through this lecture, I hope to understand the core of the Kotlin language and avoid the mistake of being blocked by the language when developing Android apps.

Through this course, you can understand Android features and set up a development environment to take your first step as an Android app developer.

You can learn the skills and know-how needed to improve work efficiency by utilizing Open API and Python.

This course is a direct lecture by the author based on the book "Python Quick Start - Python for Work - Ruby Paper (2023/04)". This course is a course to learn the basics and usage of Python, which is known to be easy to learn, so that even non-majors can use programming in real life or work. You will learn Python programming methods through real business cases rather than grammar-centered ones, and you will be able to increase the efficiency and reliability of your work by automating various business processes including HR and finance.

This course aims to acquire Python-based web crawling technology to collect data from web pages and improve data collection and analysis capabilities at work based on this.

Leveraging Python's basic syntax and popular libraries Auto-generating Python syntax and library code using ChatGPT

For the sole purpose of passing the exam, we have summarized and organized the key contents of each exam area to prepare for the AWS (AWS Certified Solutions Architect-Associate) certification.

Through this lecture, even non-majors can fully understand the concepts and operating principles of the ORM module, which is the core of Spring.

This course will enable even non-majors to develop and operate Spring MVC-based web applications.

Through this lecture, even non-majors can fully understand the concepts and operating principles of the MVC module, which is the core of Spring.

Through this lecture, even non-majors can fully understand the concept and operating principles of AOP (Aspect Oriented Programming), a core module of Spring.

Through this lecture, even non-IT majors can fully understand the concept and operating principles of IoC (Inversion of Control), the most core module of Spring.

This is a machine learning and deep learning project preparation course that uses Python to process Boston Marathon big data into the desired format and uses various charts and technologies to create valuable information.

Using pandas, we will learn data processing patterns that frequently appear in real-world situations and analyze the processed data.

This lecture is a follow-up lecture to 'Python Data-Based Stock Quant Investment Part 1'. If Part 1 was more of an introduction, Part 2 is an in-depth lecture that focuses on the entire flow of practical strategy implementation and quantitative investment development. This class focuses on advanced Pandas techniques for handling time series data, and how to implement signal-based strategies and static/dynamic asset allocation strategies that require adjusting asset weights at various intervals based on this. Furthermore, it goes beyond strategy implementation and learns about 'code framework' that directly verifies and backtests various investment strategies with minimal code modifications, how to extend this to improve it so that it can lead to actual investment, and what to watch out for in this process. In addition to the programming component, you can experience the best Python quant investment flow that you cannot find in investment books, blogs, YouTube, etc. by deeply covering theoretical contents such as the two types of return concepts (simple return, log return) and evaluation indicators related to backtesting.

You can learn the skills and know-how needed to improve work efficiency by utilizing Open API and Python.

This course aims to acquire Python-based web crawling technology to collect data from web pages and improve data collection and analysis capabilities at work based on this.

This course focuses on developing practical visualization skills that can be immediately applied in the workplace by utilizing Seaborn, a core tool for data visualization, and plotly/Dash for building interactive dashboards. Through various hands-on exercises, you can enhance both your ability to create insight-driven visual materials and implement web-based dashboards.

Learn a systematic approach to S&P 500 pair trading with Python. Lay the foundation for an investment strategy that excludes emotions through professional data analysis.

In this class, you will learn about the principles and methods of analyzing and processing various types of financial data using Python's Pandas library, apply them to situations you may encounter in the real world, and ultimately learn how to implement backtesting based on financial statement data (based on Kang Hwan-guk's book, "You Can Do Quant Investment"). As a result, you can break away from being a "passive investor" who simply follows what others say without verifying or basing the investment logic, and become a "self-directed and active investor" who can freely extract various elements necessary for strategy implementation from data and quantitatively analyze them using Python and Pandas.

A course to understand and practice machine learning that efficiently performs complex analysis and prediction! Machine learning, which seemed difficult and complicated, can be easily and enjoyably learned in its core with Maso Campus' <Python Machine Learning>.

Building Your Own Data Analysis Portfolio with Free GEMINI CLI Vibe Coding Data Collection, Analysis, Reports, and Dashboard Creation for Work Automation Even if you're new to data collection, by learning the principles, prompt writing methods, and VSCode usage, you can perform data collection, analysis, visualization, reports, and dashboard creation like a data analyst. From Python Data Collection to Analysis Report Writing with GEMINI CLI and VSCode Prompt Writing and Error Resolution Methods for Foolproof Data Collection and Analysis

The Intermediate Python course is designed to deepen your understanding of Python by covering advanced topics such as functional programming, metaprogramming, exception handling, and object-oriented programming.

Hello! Have you ever gotten tired of repeatedly writing the same code over and over again while doing data analysis? 😫 Now let's give your projects wings with PyCaret! 🚀 This course will teach you the secret to automating most of the tedious and complex machine learning processes with just 5 lines of code. Here's how this course will transform you! ✨ Master Real-World Projects: 📈Customer purchase prediction (classification), 📉Medical cost prediction (regression), 🧩Customer grouping (clustering), ⏳Airline passenger prediction (time series)! You can develop 'real' problem-solving skills by following along with 4 core projects hands-on. Acquire Expert Skills: But that's not all! 🤖 We'll also teach you expert skills like managing your models with MLOps and creating API servers with just a few clicks. Pretty amazing, right? Protect Your Work-Life Balance: Most importantly, we'll drastically reduce your repetitive tasks and help you focus only on core analysis. Leaving work on time is a bonus! 😉 Whether you're not yet comfortable with coding or you're a current professional wanting to explosively boost productivity, everyone is welcome! 👋 Shall we transform together into a '10x faster data scientist'?

In this class, you will learn techniques for retrieving (crawling) and processing data from various operating sites using Python, and apply them directly to various examples that you may encounter in the field. Through this, you will learn techniques that focus on solving practical problems based on an understanding of the basic principles of how the web works, rather than one-dimensional task automation by copying and pasting given codes, and accordingly, you will be able to collect and process data from any site in the future. You will also learn how to resolve various exceptions that occur in this process with code, and ultimately, you will be able to solve and automate various problems that occur in daily life with programming thinking.

Unlike existing lectures, this course has been newly designed based on the instructor's initial failures in learning machine learning, enabling easy understanding and application to real-world problems.

This is a basic course on Python Excel programming that you can learn with the xlsxwriter tutorial. You can analyze data and automate tasks using Python and Excel.

You can create a deep learning analysis environment in the cloud using Docker. When Docker images managed by Google, MS, etc. are combined with the cloud, you can work with the latest deep learning analysis methods on your computer.

Learn NoSQL technology, which can handle Big Data, one of the fundamental technologies in full-stack and data science utilized by modern startups. MongoDB is the easiest and quickest technology to utilize among NoSQL databases. In this lecture, you will learn the basics of MongoDB in a short time, and also acquire the skills to manipulate and utilize MongoDB with Python.

Finally, the Probability and Statistics course for everyone is here! Learn the principles of probability and statistics step by step through theory and coding practice. Reference book "Probability and Statistics for Everyone" https://wikidocs.net/book/18165

Those who have learned the basics of Python will learn how to utilize Python after watching this lecture.

Learn how to collect data from the web and create a supervised learning model for machine learning!