本番データサイエンス Part1. Python入門

The digital transformation (DT) and introduction of artificial intelligence (AI) in companies begin with the construction of machine learning models. However, the scope of machine learning technology is very broad, and in order to select the optimal method, it is necessary to clearly understand the basic concepts. In this lecture, we will introduce the core contents necessary to clearly understand the basic concepts of machine learning, focusing on five examples.

Learn why and how to handle data exploration (EDA), data cleaning, scaling, outlier handling, log transformation, and categorical encoding in real-world applications. You will also learn how to merge tabular data and process (unstructured) time series data.

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!

This is a lecture on how to use NumPy and practice its application in real-world scenarios.

Datarian's Python basics course with over 10,000 cumulative students and rich online/offline lecture experience. It boldly omits unnecessary details such as program installation so that even non-majors can learn easily, and only teaches the core of Python. This is the best course for those who are learning Python for the purpose of data analysis.

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.

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.

This course is for both beginners who have never been exposed to programming before and those who have some theoretical knowledge about Python. The main purpose of the course is to create interest and enjoyment in Python based on real projects. I hope that through this course, you will gain more ideas and have more fun with Python.

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.

🚀 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.

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 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.

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

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

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 course will teach you how to analyze data using ChatGPT and how to use Pandas. Experience the future changes that ChatGPT will create.

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.

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.

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.

Finally, a probability and statistics course for everyone is here! Learn the principles of probability and statistics step by step through theory and coding practice.

Here's how non-majors and beginners can quickly pass the Big Data Analysis Engineer practical exam! Light on theory, heavy on practice. Focus on the key points that frequently appear on the exam, centered around past questions, even without complex background knowledge.