Want to Learn More about Data Science?
As the diagram on the left suggests, there are several types of literacy that are important in data science. The logical progression is to start with basic data literacy, and once competency is achieved, move on to machine learning literacy and predictive analytics. Lastly, with competency in machine learning literacy, you are now ready for AI literacy. This is not necessarily a strictly linear process, as data scientists spend time in all three areas, depending on the demands of a project. Under the Textbook tab, we offer the textbook Health Informatics: Practical Guide and Introduction to Biomedical Data Science. These books supplement the background needed for those working in the healthcare domain.
Data literacy can be achieved in a variety of ways. Taking data science courses is advisable, plus reading and hands-on exercises with data in your domain. and adopting one of the standard approaches: 1. Programming in Python or R languages. This is advisable but associated with a steep learning curve. The use of large language models (LLMs) to create, interpret, and debug code has been a tremendous advance in data science. 2. There is a no-code approach where students can perform the same functions as programming but use platforms such as Orange or KNIME. Moving operators/widgets around performs the functions. In the case of Orange, Python is the computation engine in the background. Under the Textbook tab, we offer Data Preparation and Exploration and No Code Data Science. The three types of literacy will be covered in the workshops given. See the workshops tab.
Machine learning literacy can be achieved in the same way as data literacy but is associated with more trial and error and conducting many experiments to create and optimize prediction models. Under the Textbook tab, we offer No Code Data Science that covers predictive analytics, machine learning, supervised and unsupervised learning.
AI literacy is perhaps the most difficult of all because of complex topics and an ever-changing landscape. New LLMs are being released weekly. While a variety of asynchronous AI courses are available at variable prices, there is no live human to answer questions or provide the newest information. In-person or live virtual courses are desirable. We recommend the Board Review course by the American Board of Artificial Intelligence in Medicine. We also recommend the textbook by Dr. Anthony Change Intelligence-Based Medicine.
