My Introduction into Data Analysis

Why am I interested in data analysis?

On my journey to work with AI and Machine Learning, I have found that data analysis is a crucial skill to have. It helps you understand the data you are working with and make better decisions. I am starting to learn about data analysis and visualization tools like Pandas, Matplotlib, and Seaborn.

Getting the data ready

Since I am going to be visiting Disney World for the first time this month, I wanted to learn about the parks and rides. While this data is presented on the Disney World website. I wanted to create a dataset that I can analyze and visualize. So I used the network tab while I was on their site to download the JSON data for the parks and rides.

Here is a sample JSON from one of my runs. One of the powerful tools for data analysis in Python is Pandas. It allows you to read data from various sources and manipulate it. So my first step was to convert this JSON into data that I can easily load into Pandas DataFrame.

Here is the script I used to convert the JSON to CSV.

Cool, so now I am reading in the CSV file into a Pandas DataFrame. I can now start to analyze and visualize the data.

Analyzing the data

I started by looking at the data types of the columns and the first few rows of the data. This helps me understand the data I am working with.

Here is a notebook on Kaggle that I used to analyze the data: Disney World Activity Analysis

Side Note: I am using Kaggle to run my notebooks because it’s free and has a lot of sample notebooks for learning how to work with data. You can think of it like a Github but for Data Analysis and Cleaning. People will upload datasets they have created, other can use these datasets to perform analysis.

I really enjoyed working with this data and got more confortable with Python, Pandas, and Matplotlib. I am looking forward to learning more about data analysis and visualization in my journey of working with AI and Machine Learning.