I think this is the best lecture in terms of data analysis (loading, preprocessing, EDA, visualization). While studying Python data analysis methods and coding examples, I think, "What can I do with this?" I think this lecture provides answers and clues to that. Also, many of the methods used in parts are very useful. In addition, it was very good that it was renewed by supplementing recent data and explanations. 

Conclusion: If you want pandas, seaborn, matplotlib + @, just listen. If you are a beginner, you will never regret it.

daehynk3548

There was a rumor that Ediya would open a store near Starbucks. How different would the locations of Ediya and Starbucks be? 
Will the real estate price fluctuation trend from 2013 to 2019 be reflected in the apartment sales price? 
What kind of parks are there in my neighborhood? How can I utilize the data in the public data portal? The goal is to become familiar with Python and various data analysis libraries by handling various types of data through public data.

todaycode

Getting started with Python data analysis using public data

Thank you for your thoughtful review! Thanks to you, it has been a great help in updating all the courses up to Chapter 5. In particular, Chapter 5 has added content on analyzing and visualizing text data, such as extracting frequency from existing structured data, and implementing information masking for personal information protection using regular expressions using email, phone number, and car registration number. We will continue to update the content through feedback in the future :)

입문

초급

중급이상

파이썬

Python

판다스

Pandas

넘파이

Numpy

Preparing for data analysis

Chapter 1. Analysis of new private apartment sales prices nationwide

Chapter 2. Learning technical statistics using commercial (commercial) information

Chapter 3. Franchise Entry Analysis

Chapter 4. Comparing Starbucks and Ediya store locations

Chapter 5. Trying out various preprocessing with national urban park data

supplement