Getting Started with Python Data Analysis Using Public Data
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.
โChapter 1 Nationwide New Private Apartment Sales Price Trendsโ has been completely renewed.
We collected valuable feedback received over the past year and published it in March 2020.
The existing โChapter 1 Nationwide New Private Apartment Sales Price Trendsโ has been completely renewed.
As of Chapter 1, the explanations and visualizations have become much more detailed, from 9 videos (1 hour and 41 minutes) to 20 videos (3 hours and 25 minutes) .
It covers a wider variety of graphs than before (heatmaps, histograms, distributions, scatter plots, regression graphs, etc.) and includes content that makes it easy to draw subplots.
We also provide practice code and result code.
Try using the practice file ( 01-apt-price-input.ipynb ) that provides a simple guide to follow along with the code while watching the video, and the file (01-apt-price-output.ipynb) that displays the results .
Please refer to the video introduction for the code location and google colaboratory path!
Tutorials for other chapters will also be renewed in March 2020!
thank you
