How to Build an Efficient Startup Workflow System with Just Notion 🧑🏻‍💻 (Practical Workflow Templates Provided by Practitioners)

Say goodbye to complicated collaboration tools! 👋 From Markdown to databases, the essential skills of a workaholic can be done with Notion 🚀

Django Base Camp is a course where you can systematically learn the core of the Django web framework from beginner to intermediate level. You can quickly develop the ability to build web applications through project-oriented, practical classes.

No more toy projects! We're revealing all the secrets to building a secure authentication system with JWT and Redis, actually used by developers in the field.

This course systematically teaches from JavaScript synchronous/asynchronous concepts, asynchronous processing methods incorporating the latest syntax such as Promise and async/await, and practical HTTP communication.

King Basic🐥, only for those who say they have no more words to describe me.👂 Concepts go to the universe🚀, King Basic practice day and night✍, learn SQL with your hands!✋ No installation required.🔥 Here is the lecture restaurant, Jecobe!🎉

Learn the fundamentals of the web, networks, and HTTP that every developer should know, in a code-based, real-world, data-driven way.

Essential for web development! Learn JavaScript, a popular language widely used in various fields, with fundamental concepts and practical examples!

This is a basic computer course for those who don't know much about computers. You can understand the components of a computer and the uses of computer parts through perfect analogies.

Generative AI for Designers🧙‍♂️ We reveal practical know-how to maximize design work efficiency with generative AI like Canva and Midjourney.

A basic textbook and practical guide covering principles and practical uses of various generative AIs like ChatGPT and Claude, and for their effective introduction and utilization.

Can you answer the question, "Why do we need classes? 🤔" This covers everything Python developers should know about Python classes!

New to both Blender 3D and 3D in general? A 3D KakaoTalk emoticon creator will directly teach you in this beginner-friendly 3D introductory course.

You can easily create your own blog with just GitHub Fork! This is a service that allows you to manage a learning blog (TIL) and GitHub lawn together.

If you want to create a web page, learn HTML/CSS. You can implement various contents and designs with HTML, which defines the structure of the web page, and CSS, which defines the style of the web page.

Python Basecamp is a course for beginners who want to learn the basic concepts of Python. Improve your skills through basic concepts and practical examples.

Python made easy! Learn Python like a game with various stories in 'Winib World'!

Would you like to experience web server development? We will help both non-majors and front-end developers take their first step into server development. Let's efficiently explore servers and databases using familiar JavaScript syntax all at once, and understand servers!

This is a tutorial lecture where you can learn simple usage of PyScript.

Know it! Well! Just! Neatly! Sensefully! This is a free lecture from the book Flex & Grid. You can use the open source link, the book, and the Notion link for free. This is a non-profit project, so using the textbook is possible without permission.

Learn how to work efficiently using Python, a basic programming language.

This is not an investment lecture. It covers data collection, analysis, and visualization using stock market data. You will work with various data formats and learn various text preprocessing techniques. It covers visualization techniques for time-series data, methods for representing scales, and several techniques for interpreting stock price data. This course is about learning how to gain insights through data analysis. It is structured so that the concepts learned through stock price data can be applied to collecting, analyzing, and visualizing other time-series data such as demand, inventory, sales, and traffic volume.

No more encyclopedia-like lectures from A to Z! A reference lecture that only collects the best that can be applied directly to real life!

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.

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.

Based on the instructor's own failed experiences when first learning machine learning, this is a newly designed course that differs from existing lectures, making it easy to understand machine learning and apply it to real-world problems

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.

We guide non-majors and beginners to quickly obtain the Big Data Analysis Certification (Practical Exam)! Keep the theory light and the practice solid—focusing on core points that are guaranteed to appear on the exam through past questions, without the need for complex background knowledge.

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>.

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.

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.

* 100% Practical Curriculum: Focused on hands-on data handling rather than grammar-based learning * Essential Machine Learning Prep: A complete summary of data preprocessing techniques required before AI training * Data Analysis Tool Mastery: Conquer core functions of Numpy (numerical operations) and Pandas (data manipulation) * Deriving Visualization Insights: Transferring data visualization skills using Matplotlib and Seaborn * Building Statistical Foundations: Learning basic statistics and group analysis techniques to identify data characteristics

Let's learn how to collect data from the web and create a supervised learning model for machine learning!

Make AI data analysis and modeling results into a Web Program.