We recently posted about why digital health researchers should consider using Python to work with their data. Python offers so much power and flexibility, such as automating cleaning and processing data and sharing reproducible notebooks.
If you are getting started with Python and feel overwhelmed, know that a large and supportive Python community is waiting to help you. Here is our quick list of good resources for researchers to reference:
- Python tutorials: Online tutorials and courses can help you with the basics of using Python, including Codecademy, Coursera, and DataCamp. These resources provide a gentle introduction to the basics of the language and can help you get up to speed quickly.
- NumPy, Pandas, and Matplotlib: These are some of the most popular Python libraries for data analysis and cleaning. NumPy provides support for array-based calculations, Pandas makes it easy to work with data in a tabular format, and Matplotlib offers a range of tools for creating visualizations.
- scikit-learn: This is a machine-learning library for Python that provides a wide range of algorithms for classification, regression, clustering, and dimensionality reduction. It’s a powerful tool that can help you make predictions and analyse complex data sets. You can find more information about scikit-learn here.
- TensorFlow and PyTorch are two of Python’s most popular deep-learning frameworks. They provide a range of tools and functions for building and training neural networks and can be used for a wide range of tasks, including image classification, natural language processing, and recommendation systems. You can learn more about TensorFlow and PyTorch at their respective websites.
- Jupyter Notebooks: Jupyter notebooks are a powerful tool for data analysis and cleaning, allowing you to create sharable notebooks with your code, data, and results. They are a great way to share your work with others and can be used to create interactive dashboards and visualizations. You can download Jupyter Notebooks and find more information about them here.
- Python for Data Science Handbook: This is a comprehensive guide to using Python for data science and provides a range of tips and tricks for working with data in Python. Whether you are a beginner or an experienced user, this handbook is an essential resource for anyone looking to improve their data analysis and cleaning skills. You can access the Python for Data Science Handbook here.
These resources should give you a good starting point for using Python in your research. Stay tuned to this blog as we will continue sharing tips for digital health researchers looking to improve their data science skill sets.