Many PhD research scholars make use of simulation and coding tools for their research projects, especially for analysing their quantitative data. Python is one of the most widely used tools for programming while handling complex research data. If you are also planning to use Python for your PhD research project but are confused as to how to go ahead, then we help you understand a few steps you may take to get started. Check out.
Steps to get started with Python
Know Python: It is highly important to know the usage of a tool before you benefit from it. Thus, you should start by learning the basics of Python language syntax and data structures. It is fast and easy to learn programming in Python and coding towards effective data analysis. Python is rich in terms of examples and open source libraries that you can use to your advantage in PhD research. If you have not used this tool earlier, you may take up a short-term course to being operational on it.
Practice coding: When you are good at programming, you can start creating codes for your research. Before handling complex tasks, try making some codes for data filtering, organisation or plotting. While data analysis is the most useful application of simulation codes, you can record the entire pipeline of your research in order to make it reproducible.
Use libraries: There are many wonderful things you can do with the libraries in Python. Explore the ones you can use or combine to work in your research. This will minimize the work you will need to do yourself. It is easy to import libraries in Python.
Explore new ideas: Whether you need to gather some data online or manipulate the data, you can be explorative with coding to your benefit. In fact, you may use this tool to collate just any type of data you may need from the Web. Python is great when you want to process or modify your research data. Thus, start finding its real-world applications and give more richness to your research while also saving your time.
Use for data visualization and analysis: Finally, you may use various available computing modules that make Python more powerful with enhanced capabilities. You may use it with Stata, R, and such other statistical programs for meaningful data analysis and data visualization.
Thus, you may follow the above steps and go from learning Python and coding for data analysis to performing several time-saving tasks in your research. However, some of you may not know the basics of programming or may not have time to learn programming in Python. If you think you cannot use Python effectively for your PhD project, then an alternative for you is to simply follow the step of seeking guidance from a professional statistical consultant. An expert statistician can help you with writing codes and finding new ideas that can broaden the scope of your PhD research.