Breaking down perceived barriers to Python data analysis for the academic researcher
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Many researchers would benefit from using the rich tools available in Python for data management, analysis and visualization. However, the tools remain underutilized in this group. This poster breaks down the perceived barriers to entry and identifies ways the Python community can make data in Python more approachable to researchers from a variety of fields.
It is commonly known that experts can not remember the areas that baffle and frustrate novices in a field. By identifying areas that are problematic for new comers to Python data analysis, we can provide better resources for those individuals and reduce barriers to entry. As an academic researcher who has recently transitioned from using proprietary software for statistical programming to using Python, I have experienced both the benefits and difficulties that come with adopting a new programming language. This poster will address how the Python data community can identify and address real and perceived barriers, and improve adoption of Python by the wider research community. The reasons researchers may hesitate to adopt Python generally fall into 3 categories: 1) lack of understanding of the power of the data tools available 2) technical programming inexperience 3) difficulties related to going against the field specific norms. Through directed resources and communication from the Python data community, many of these barriers can be overcome. This poster will specifically address key issues in each category (e.g. publication concerns in category 3) and suggest potential solutions. The conversations sparked by identifying some of the needs of researchers during this poster session will help us find clear strategies to encourage wider adoption of the rich Python data tools.