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Data Analysis Introduction

Data Analysis Introduction#

Learning Objectives#

In this lesson, we will:

  • Introduce the data analysis task

  • Create a project folder for the task, download some data, and place this into it.

Scenario: A miracle arthritis inflammation cure#

Our imaginary colleague “Dr. Maverick” has invented a new miracle drug that promises to cure arthritis inflammation flare-ups after only 3 weeks since initially taking the medication! Naturally, we wish to see the clinical trial data, and after months of asking for the data they have finally provided us with a CSV spreadsheet containing the clinical trial data.

The CSV file contains the number of inflammation flare-ups per day for the 60 patients in the initial clinical trial, with the trial lasting 40 days. Each row corresponds to a patient, and each column corresponds to a day in the trial. Once a patient has their first inflammation flare-up they take the medication and wait a few weeks for it to take effect and reduce flare-ups.

To see how effective the treatment is we would like to:

  • Calculate the average inflammation per day across all patients.

  • Plot the result to discuss and share with colleagues.

3-step flowchart shows inflammation data records for patients moving to the Analysis stepwhere a heat map of provided data is generated moving to the Conclusion step that asks thequestion, How does the medication affect patients?

Download the data#

First, we need to open JupyterLab, and create a new folder for our project. In this folder, lets create a new folder called data. We can then download some data from here.

Unzip this folder, and move the .csv files into the same folder as your Jupyter notebook, or JupyterLab instance. This can be done by dragging them across onto the sidebar.

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