This course on Data Science aims to teach learners how to set up a data science project structure, use tools like Github, Colab Notebook, and Pandas, analyze and visualize data, merge dataframes, and implement continuous integration with Github Actions. The teaching method involves practical demonstrations and hands-on exercises. This course is intended for beginners in data science looking to learn essential tools and workflows for data analysis and project management.
Overview
Syllabus
Intro
Data Science Project Structure Overview
Create Github Repo
Launch Github Codespaces
Using Colab Notebook
Using TOC in Colab Notebook
Saving notebook to Colab to Github
Ingesting CSV files into Colab
Describing Data using df.describe
Plotting data with seaborn
lmplot
Comparing cumulative deaths in Covid19 plot by state
Merging Pandas Dataframe with election and Sugar Consumption
Exporting CSV file and uploading to Github from Colab result
Continuous Integration of Jupyter Notebook with Github Actions
Create Makefile
Using Github Actions to test Jupyter via nbval plugin
Using Github Status Badge for Jupyter Notebook test run pass
Taught by
Pragmatic AI Labs
