[리뉴얼] 처음하는 파이썬 데이터 분석 (쉽게! 전처리, pandas, 시각화 전과정 익히기) [데이터분석/과학 Part1]

For those who want to learn data science, big data, and web scraping, this course will: (1) organize the most essential Python syntax in a short amount of time, and (2) familiarize you with Python and web scraping through hands-on web scraping programming.

Through this course, you will learn to understand SQL and databases, SQL grammar, database design for practical use, database use through programming, and data analysis use cases.

You will learn NoSQL technology, one of the basic technologies of full stack and data science technology used in the latest startups, which can handle big data. MongoDB is the easiest and fastest NoSQL technology to use. In this lecture, you will learn the basics of MongoDB in a short period of time and learn how to use MongoDB with Python.

This is a full-stack series course aimed at beginners, with the goal of developing web and app services capable of actual commercialization, based on experience developing real web services in the field. As the first step (part 1-1) towards this, it is a course that explains in detail intermediate Python for full-scale development, backend implementation technology using Flask, and essential web technologies (HTTP, RestAPI, MVC pattern).

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.

This lecture is Part 3 of the Full Stack Lecture Series and is designed to help you master the latest server technology and Docker technology. This lecture is designed to help you practically incorporate Docker into your tech stack by meticulously testing various Docker options, learning related technologies such as Linux that are necessary for understanding Docker, and even building complex services.

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 course is a complete course that will help you learn Flutter step by step, from the basics of Dart programming to basic and intermediate Flutter functions, and even linking the backend Firebase to actual projects!

This course is redesigned, unlike existing courses, to help you easily understand machine learning and apply it to real-world problems, based on the instructor's own failures when they first learned machine learning.

This is the Part 1 lecture of a roadmap that neatly organizes the absolutely necessary core of Computer Science (CS knowledge) for doing development and data work well, in a short time. 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 real-world practice, from system SW to web and app development and data science, this lecture neatly summarizes the absolutely necessary core for doing development and data work well, step by step, according to standard principles, but easily.

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

This course is newly designed to help you learn deep learning step-by-step, covering mathematics, theory, PyTorch-based implementation, transfer learning, and even GPT's core Transformer, based on the instructor's own failed experiences when first learning deep learning.

This course is redesigned, unlike existing courses, to help you easily understand machine learning and apply it to real-world problems, based on the instructor's own failures when they first learned machine learning.

You will learn NoSQL technology, one of the basic technologies of full stack and data science technology used in the latest startups, which can handle big data. MongoDB is the easiest and fastest NoSQL technology to use. In this lecture, you will learn the basics of MongoDB in a short period of time and learn how to use MongoDB with Python.

Through this course, you will learn to understand SQL and databases, SQL grammar, database design for practical use, database use through programming, and data analysis use cases.

This course will help you quickly learn the core concepts of Python and the Pandas library. Improve your programming skills by solving the practice problems at the end of each part!

Son Heung-min's 100th goal project! Practical crawling & data analysis of Transfermarkt

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.

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 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.

With Python Quant Investment, you can create investment strategies based on data and invest according to the strategy. You can implement various asset allocation strategies and ultimately create your own investment strategy.

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.

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!

Time series lecture to become a data analysis expert! Let's become an analyst strong in theory and practice!

Revolutionize your stock investing 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 mock investing using the Korea Investment & Securities API, and then perform AI stock automated trading with a real account. Even stock beginners are welcome. We will easily explain the basic concepts of stocks and why you should invest in US stocks, and provide all source code and remote support. From now on, investing is essential in the next 60 years of our lives. Build your own AI investment partner that will be with you!

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.

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.

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

This course is a course that practices the basics of machine learning mathematics and statistics using Python. Machine learning and deep learning require programming skills, but they also require a mathematical background to understand the principles. Through this course, you can gradually learn the basic mathematics required for artificial intelligence.

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>.

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.