Becoming a health data scientist can be challenging, but it is an incredibly rewarding career choice. The rapidly growing field of health data science merges statistical analysis, data visualization, and machine learning to gain insights from healthcare data. However, becoming a health data scientist can be costly.
In this article, I present a wide range of resources, such as online courses, textbooks, and tutorials, that are available to help you acquire the necessary skills to become a health data scientist. Whether you are a beginner or just looking to deepen your understanding of health data science, these resources will provide you with a solid foundation.
From our partners:
Offered by the University of Colorado
This Clinical Data Science Specialization course on Coursera provides an overview of clinical data science and its applications to healthcare. This series of six courses covers concepts of clinical data models and common data models, natural language processing, and the fundamentals of transforming clinical practice using predictive models. This is a great starting point for better understanding healthcare data science.
Offered by MIT with instruction from Prof. Peter Szolovits and Prof. David Sontag
This Machine Learning for Healthcare class through MIT Open Courseware gives a comprehensive introduction to machine learning techniques and their applications to healthcare. Students will learn about causal inference, natural language processing, as well as reinforcement learning and how it applies to healthcare. This course is suitable for healthcare professionals, data scientists, and students that are interested in the various applications of machine learning to the healthcare industry.
Written by Andrea Hobby
The Health Data Science Newsletter on Substack has a range of resources for those looking to get started in this area. It also curates a list of courses and tutorials for enthusiasts to learn more about health data science.
By Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
This introduction to statistical learning is a widely used and highly recommended textbook for data scientists and provides a clear and comprehensive introduction to statistical learning methods. All of the concepts in the book apply to health data science work. The book covers a wide range of topics, including linear and non-linear models, regularization, and model selection. It is an excellent resource for both beginners and experienced health data scientists.
Offered by Stanford University
This AI in Healthcare Specialization course on Coursera provides a great introduction to the application of artificial intelligence to healthcare. You can learn about the fundamentals of machine learning in healthcare and its various applications. This course is helpful for healthcare professionals, data scientists, and anyone interested in the intersection of AI and healthcare.
Offered by UC Davis
This Health Information Literacy for Data Analytics course on Coursera is designed for health professionals and students to understand how to find, evaluate, and effectively use health information. It covers essential health information literacy and data analytics concepts including data management, data visualization, and the critical thinking skills needed to make informed decisions. This course would suit anyone interested in understanding health information and data analytics.
From Mode Analytics
This SQL tutorial for Data Analysis from Mode Analytics is a comprehensive guide to help beginners learn the basics of SQL. It covers SQL’s fundamental concepts and commands, including data manipulation and data querying. This tutorial is an excellent resource for anybody who wants to learn how to interact with databases and perform data analysis using SQL.
By Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong
You can never underestimate how essential mathematics is in machine learning and it is one area that takes a lot of time to grasp. This book covers a variety of topics including Linear Algebra, Analytic Geometry, Matrix Decomposition, Linear Regression, Dimensionality Reduction with PCA, Density Estimation, and Classification with Support Vector Machines.
With MITx & edX
This Collaborative Data Science for Healthcare course is a program that provides an in-depth understanding of how health data professionals can apply data science to healthcare data. The course covers a range of topics such as principles of data science as applied to health, analysis of electronic health records, and artificial intelligence and machine learning in healthcare. The course is suitable for healthcare professionals and data scientists looking to gain the skills and knowledge needed to work effectively with healthcare data. The course includes interactive exercises, quizzes, and real-world examples to provide hands-on experience for those who plan to work with healthcare data.
Offered by DeepLearning.AI
This AI for Medicine Specialization on Coursera covers topics such as AI for Medical Diagnosis, AI For Medical Treatment, and AI for Medical Prognosis. The course is designed for healthcare professionals, students of healthcare, and data scientists who want to learn more about the use of AI in medicine. The classes provide firsthand learning through quizzes, assignments, and projects, where you can learn to apply neural networks and risk prediction models to real-world medical data.
For enquiries, product placements, sponsorships, and collaborations, connect with us at [email protected]. We'd love to hear from you!
Our humans need coffee too! Your support is highly appreciated, thank you!