The easiest introduction to data analysis for non-majors

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'.

Everyone's dream blog! Create a differentiated blog that you can make and operate yourself, rather than a blog that you can see anywhere! Challenge yourself now with a Github blog!

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.

From the basics of Numpy and Pandas to data preprocessing, visualization, and crawling, all in one place! This is an introductory Python course for data analysis.

The content is designed for even non-majors to easily learn and apply Python to their work, covering everything from environment setup to basic programming concepts, libraries, and language usage through hands-on practice.

Learn about the basics of Python required for data analysis, the data analysis framework (pandas), and the data analysis process.

Python Programming for Non-Majors

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.

The first class in the curriculum, "Anyone Can Learn Python", is an introductory programming course for those with no programming/coding experience. Based on my experience with numerous classes taught to non-majors, this class focuses on the "story" and "principles" from the structure of a computer to how programs work and the role of programming languages in this process, allowing beginners to approach the unfamiliar concept of programming more familiarly. In addition, by adopting the Python language, which has recently been growing at an incredible rate, the barrier to entry for programming has been lowered even further, and when explaining the grammatical elements of the language, the focus is on "Why" and "How" rather than "What", so that it is not a simple grammar memorization class, but rather on real-world examples and practical applications. Based on the incorrect learning methods that beginners often use, it also covers tips on how you should view and learn programming in the future, and based on the trial and error I experienced while struggling and wasting time while teaching programming on my own in the past and the inner strength I gained from conducting offline classes for many years, it focuses on what beginners find difficult and how to convey it, so it will be of great help to those who are hesitant about getting started with Python.

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.

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

AI does the coding, I get the results! Maximize practical programming skills, achievable even for beginners!

This is a Python coding test lecture centered on problem solving, tailored to those who are new to coding tests.

Anyone can easily create a Python game using the Pygame library, starting from zero base.

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.

Hello! Have you ever gotten tired of repeatedly writing the same code over and over again while doing data analysis? 😫 Now let's give your projects wings with PyCaret! 🚀 This course will teach you the secret to automating most of the tedious and complex machine learning processes with just 5 lines of code. Here's how this course will transform you! ✨ Master Real-World Projects: 📈Customer purchase prediction (classification), 📉Medical cost prediction (regression), 🧩Customer grouping (clustering), ⏳Airline passenger prediction (time series)! You can develop 'real' problem-solving skills by following along with 4 core projects hands-on. Acquire Expert Skills: But that's not all! 🤖 We'll also teach you expert skills like managing your models with MLOps and creating API servers with just a few clicks. Pretty amazing, right? Protect Your Work-Life Balance: Most importantly, we'll drastically reduce your repetitive tasks and help you focus only on core analysis. Leaving work on time is a bonus! 😉 Whether you're not yet comfortable with coding or you're a current professional wanting to explosively boost productivity, everyone is welcome! 👋 Shall we transform together into a '10x faster data scientist'?

Strengthen your coding skills through coding test problems by learning Python-based data structures and algorithms!

A course where you can not only build programs required for blog operation using Python, but also earn additional revenue through sales and branding.

Solve coding test problems using 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.

You can systematically learn stock trading automation using Python and the Interactive Brokers API, which holds the top spot in global market share.

Learn Python coding. Learn design scripting with Rhino3d Grasshopper. Learn creative coding such as computational thinking, parametric, and generative design.

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.