Data-Visualisation-with-Python

Data visualization refers to the techniques used to communicate data or information by encoding it as visual objects (e.g., points, lines or bars).

The main goal of data visualization is to communicate information clearly and effectively through graphical means.

Getting Started with Jupyter Notebooks

Jupyter notebooks are an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text.

In this workshop we will use IBM Watson Studio to run a notebook. For this you will need an IBM Cloud account. The following steps will show you how sign up and get started. When you have the notebook up and running we will go through the notebook.

IBM Cloud

• Sign up for an IBM Cloud account

• When you are signed up click Create Resource at the top of the Resources page. You can find the resources under the hamburger menu at the top left:

• Search for Watson Studio and click on the tile:

• Select the Lite plan and click Create.
• Go back to the Resources list and click on your Watson Studio service and then click Get Started.

IBM Watson Studio

1. Create a new Project

• You should now be in Watson Studio.
• Click on the Projects option to create a New project.

• Select an Object Storage from the drop-down menu or create a new one for free. This is used to store the notebooks and data. Do not forget to click refresh when returning to the Project page.
• click Create.

4. Load and run a notebook

• Add a new notebook. Click Add to project and choose Notebook:

• Choose new notebook From URL. Give your notebook a name and copy the URL https://github.com/IBMDeveloperUK/Data-Visualisation-with-Python/blob/master/Notebook/Data_Viz.ipynb
• Select the custom runtime enviroment that you created and click Create Notebook.
• The notebook will load.

You are now ready to follow along with the workshop in the notebook!

Data Sources and other References.

• Cases Dataset : https://coronavirus.data.gov.uk/cases
• Cars Dataset : https://www.kaggle.com/fivethirtyeight/fivethirtyeight-bad-drivers-dataset
• IGN Scores : https://www.kaggle.com/alexisbcook/data-for-datavis?select=ign_scores.csv
• Coffee and Code Dataset : https://www.kaggle.com/devready/coffee-and-code
• Flights Dataset: https://github.com/mwaskom/seaborn-data/blob/master/flights.csv
• Music by Genre Dataset :https://www.kaggle.com/yamaerenay/spotify-dataset-19212020-160k-tracks?select=data_by_genres.csv
• https://matplotlib.org/tutorials/index.html
• https://seaborn.pydata.org/examples/index.html
• https://github.com/datasciencescoop