Version Control for analytic projects

Question

I've found several things about how to manage a data science projet with GIT but I didn't find something about how to manage a set of projects.

In 90% of the case I'm working alone and over the month a lot of people ask me to check:

• the performance of our marketing operations
• the impact on sales of special period like christmas.
• clustering of our customers
• simple predictive models (churn,...)

Here is my typical workflow for a single project:

1. Prepare the data in SQL
2. Make descriptive and predictive analysis in R/Python. I often use my own library of code which I update over the time
3. Create output results in Markdown or powerpoint presentation.

Here is the folder organisation for each project:

1. Data
   • base
   • processed

2. R scripts

3. Python scripts

4. Outputs (figures, markdown, powerpoint,...)

And two libraries of code in R and Python that I use for all the projects.

Question: In this case what is the best strategy ?

1. A single repository with all the projects because the libraries are shared among several projects ?

If yes, is it ok to have dozen of branches in the same repository like:

• R_library_prod
• R_library_dev
• Python_library_prod
• Python_library_dev
• clustering_2015_prod
• clustering_2015_dev
• christmas_sales_analysis_prod
• christmas_sales_analysis_dev

• and so on

  2. A repository for each project ? (with potentially only 2 branches: prod and dev)

If yes, how to manage the updates of the R and Python libraries ? Should I have a distinct repo for them and updates the libraries manually in the analytics projects repositories ?