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The Software and Data Carpentry organizations have a mission of making it easier for scientists and data analysts in academia to replicate and review each others work. In order to achieve this goal they conduct training and workshops that teach modern best practices in software and data engineering, including version control and proper data management. In this episode we had the opportunity to speak with Maneesha Sane, the program coordinator for both organizations, so that we could learn more about how these projects are related and how they approach their mission.
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- We are recording today on November 10th, 2015 and your hosts as usual are Tobias Macey and Chris Patti
- Today we are interviewing Maneesha Sane about Software Carpentry and Data Carpentry
Interview with Maneesha Sane
- How did you get introduced to Python?
- Can you explain what the Software and Data Carpentry organizations are and what their respective goals are?
- What is the history of these organizations and how are they related?
- What does a typical Software Carpentry or Data Carpentry workshop look like?
- What is the background of your instructors?
- Can you explain why Python was chosen as the language for your workshops and why it is such a good language to use for teaching proper software engineering practices to scientists?
- In what ways do the lessons taught by both groups differ and what parts are common between the two organizations?
- What are some of the most important tools and lessons that you teach to scientists in academia?
- Do you tend to focus mostly on procedural development or do you also teach object oriented programming in Software Carpentry?
- What is the target audience for Data Carpentry and what are some of the most important lessons and tools taught to them?
- Do you teach any particular method of pre-coding design like flowcharting, pseudocode, or top down decomposition in software carpentry?
- What scientific domains are most commonly represented among your workshop participants for Software Carpentry?
- What are some specific things the Python community and the Python core team could do to make it easier to adopt for your students?
- What are the most common concepts students have trouble with in software & data carpentry?
- How can our audience help support the goals of these organizations?