[TeddyNote] Pandas in One Shot (Includes E-book)

Build and share your own data analysis app quickly and easily using Streamlit!

This is a free lecture from TedNote's 'Note Series'. This lecture is intended for Python beginners. We provide a separate e-book textbook so that you can learn even if you follow the daunting Python study. You can find more lectures on the YouTube channel 'TedNote'.

A blog of your own that everyone dreams of! Not just another common blog you see everywhere, but a differentiated blog that you create and manage yourself! Try it now with a Github blog!

Data analysis sounds a bit grandiose, but it is actually a process of establishing a hypothesis and verifying it based on data. You can learn how to analyze what data to verify your hypothesis using Python.

This is a course for beginners to master the fundamental techniques of the entire Python data analysis process. As an e-commerce planner and developer currently applying data analysis techniques in the field, I have designed this course to help you easily learn and immediately apply the entire Python data analysis workflow.

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

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

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.

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

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.

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.

A course to understand and practice machine learning for efficiently conducting complex analysis and predictions! Even machine learning, which may seem difficult and complex, can be easy and fun to master with Masocampus's <Python Machine Learning>.

* 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!