Curiosity satisfied!
Today marked the day of my first experience with treating statistical data using Python and Google Colab, applying Descriptive Statistics and Pearson Correlation.
Pre-requisite for this documentation: Data coding has been done.
Data coding is typically the first step in preparing raw research data for analysis. It involves converting raw data into a structured format that can be easily analyzed. This often includes:
Assigning numerical codes to categorical variables (e.g., gender, race, yes/no responses).
Transforming open-ended responses into standardized categories or codes.
Organizing the data into a consistent format (e.g., spreadsheets, databases) for further analysis.
This step ensures that the data is ready for further statistical treatment, such as cleaning, descriptive statistics, and modeling.
Please feel free to make ChatGPT your guide to customize the coding based on the desired scope of the descriptive statistics.
Please feel free to explore Google Colab and Python using this How-To Document.
Please feel free to explore where and how to secure datasets for your practice.
To do: I will try ordering a dataset in the future.
Important note: Refrain from using Python via Google Colab for highly sensitive Live Statistical Data. Please use a locally installed Python instead for your research studies involving Live Data.
Thank you for your kind support.
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