Learn Data Science with Kaggle Practice

Creating your own data analysis portfolio with Google Antigravity CLI harness engineering and vibe coding: data collection, analysis, reporting, dashboard creation, and task automation. Even if you are new to data collection, once you master the principles, prompt engineering, and how to use Antigravity, you can perform data collection, analysis, visualization, reporting, and dashboard creation just like a data analyst. From Python data collection to report writing learned with Antigravity IDE and CLI. Prompt writing and error resolution methods for foolproof data collection and analysis.

There was a rumor that Ediya Coffee always opens its stores near Starbucks. How much of a difference is there really between the store locations of Ediya and Starbucks? Will the real estate price fluctuation trends from 2013 to 2019 be reflected in apartment pre-sale prices as well? What kind of parks are in my neighborhood? How can we best utilize the data available on the Public Data Portal? The goal is to become familiar with Python and various data analysis libraries by working with different types of data through public data.

Python Korean Text Analysis and Natural Language Processing: Word Cloud Visualization, Morphological Analysis, Topic Modeling, Clustering, Similarity Analysis, Bag of Words and TF-IDF for Text Data Vectorization, Text Classification Using Machine Learning and Deep Learning, and How to Use Hugging Face

What is Kaggle? Every year, Kaggle conducts a survey of users around the world. When you want to learn data science, you may have many questions. Is it too late to start? Which language should I choose between Python and R? How much is the salary? Find out by reading the responses from users around the world! With just a few powerful features of Pandas, you can perform frequency analysis and data visualization.

This is not an investment lecture. It covers data collection, analysis, and visualization using stock market data. You will work with various data formats and learn various text preprocessing techniques. It covers visualization techniques for time-series data, methods for representing scales, and several techniques for interpreting stock price data. This course is about learning how to gain insights through data analysis. It is designed so that the concepts learned through stock price data can be applied to collecting, analyzing, and visualizing other time-series data such as demand, inventory, sales, and traffic volume.

When using Pandas for data analysis, preprocessing, machine learning, and deep learning, we often find ourselves relying on the same core functions. There is a Pandas Cheat Sheet that compiles only these essential features. For those who have grown tired of trying to learn Pandas through thick books, this two-page document will teach you Python Pandas. Master the core functions of Pandas using the cheat sheet provided by the official Pandas tutorial.

What is Kaggle? Every year, Kaggle conducts a survey of users around the world. When you want to learn data science, you may have many questions. Is it too late to start? Which language should I choose between Python and R? How much is the salary? Find out by reading the responses from users around the world! With just a few powerful features of Pandas, you can perform frequency analysis and data visualization.

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