[リニューアル] はじめてのPythonデータ分析 (簡単に！前処理、pandas、可視化全過程を身につける) [データ分析/科学 Part1]

This course offers an easy and fast way to learn FastAPI, the latest full-stack and backend framework, along with cleanly organized materials and projects that cover backend concepts, core FastAPI syntax, and asynchronous database utilization in an easy, step-by-step manner.

For those looking to learn Data Science, Big Data, and Crawling, (1) we will summarize the most essential Python core syntax in a short time, and (2) you will become familiar with Python and crawling through practical crawling programming.

This newly designed course, drawing from the instructor's initial deep learning failures, helps you progressively learn deep learning essentials: math, theory, PyTorch-based implementation, transfer learning, and GPT's core Transformer.

This course, Part3 of the Full-stack Course Series, solidly teaches the latest server and Docker technologies. It's structured to practically integrate Docker into your tech stack, meticulously testing its options, learning necessary related tech like Linux, and building complex services.

Through this lecture, you can learn about understanding SQL and databases, SQL syntax, database design for practical application, database utilization through programming, and even data analysis application cases.

This is Part 1 of a roadmap lecture that concisely organizes the essential computer science (CS knowledge) needed to excel in development and data, in a short period. This lecture has also been used as an in-house lecture at top domestic companies for several years. Based on 20 years of experience in actual practice, from system SW to web, app development, and data science, this lecture systematically yet easily organizes the essential core knowledge needed to excel in development and data.

Based on real-world web service development experience, this full-stack series targets beginners, aiming to develop web and app services capable of actual commercialization. As the initial step (part1-1), this lecture details intermediate Python for full-scale development, backend implementation with flask, and essential web technologies (e.g., HTTP, RestAPI, MVC patterns).

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.

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.

Master advanced SQL, solve 101 practical data analysis problems at a professional level together, and build a solid SQL data analysis skillset.

This is a full-stack lecture series based on real-world web service development experience, aiming to help beginners develop web and app services to a level suitable for actual commercialization. As Part 2 of this series, this lecture thoroughly and solidly covers the latest HTML5, CSS3, vanilla Javascript + ES6, considering responsiveness and compatibility within the diverse landscape of frontend technologies.

This comprehensive course allows step-by-step Flutter learning, from Dart programming basics to Flutter's basic/intermediate features, and integrating backend Firebase for real projects.

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.

Learn Pandas' core features, comparing with SQL!

Pandas is one of the most widely used libraries in Python. Pandas, which has become an essential element for data analysis, you can learn everything about Pandas, the essential library for Python data analysis, through the <Pandas in One Shot> lecture.

Through this lecture, you can learn about understanding SQL and databases, SQL syntax, database design for practical application, database utilization through programming, and even data analysis application cases.

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.

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

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.

Introductory lecture on recommendation systems by Datalian, who has over 10,000 cumulative students and rich online/offline lecture experience. Learn the basic theory of recommendation systems and practice movie data analysis together.

I want to create an algorithm trading bot that makes unlimited money and run it automatically. Where should I start?

In Part 3 of Creating a Python Trading Room for Quant Investment, you will learn about portfolio theory and concepts necessary for real investment, as well as how to implement them with code. In Parts 1 & 2, you mainly learned about code implementation methods for analyzing financial data, and in Part 3, you will learn about 'portfolios', which are an important part of quantitative investment. With the question of why you should create a portfolio, you will learn portfolio theory, implement it in Python, and practice the code necessary for real portfolio investment. I hope this lecture will be helpful for your own portfolio investment.

Time Series: Become a Data Analysis Expert! Let's be analysts strong in theory & practice!

Revolutionize your stock investments with AI! In this lecture, you will directly build a real, working AI automated trading system targeting the top 25 NASDAQ stocks. We provide a special opportunity to gain experience and know-how through simulated investments with the 한국투자증권 API, and to perform AI stock automated trading with a real account. Even stock beginners will have no problem. We will easily explain basic stock concepts and why you should invest in US stocks, and provide all source code and remote support. From now on, investment is essential for the next 60 years of our lives. Create your own AI investment partner that will be with you!

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.

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.

Now display Python results as web pages! You can create your own web page using only Python. This is an opportunity to create an Interactive Dashboard that allows you to check results created with Python, such as data analysis and machine learning, on a web page!

I will guide non-majors and beginners to quickly pass the Big Data Analysis Engineer practical exam! Theory lightly, practice thoroughly. Even without complex background knowledge, focusing on past questions, we intensively study only the points sure to appear on the exam.

🚀 Practical AI Anomaly Detection with Databricks! 💡 Stop using expensive and slow models! From large-scale data optimization to real-time deployment, complete an anomaly detection AI that can be used immediately in corporate practice.

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.

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

This lecture is for beginners who are new to coding, as well as those who want to try out various uses of Python, covering basic concepts of Python, Numpy, Pandas, Plotly, Django, and crawling, as well as simple data analysis.