Clinical Data Science

Clinical Data Science

Introduction to Clinical Data Science

Are you curious about the use of data produced by medical professionals, nurses, and the healthcare system to enhance the treatment of patients in the future? If so, you might become a clinical data scientist someday!

With this specialisation, students gain practical experience using informatics tools and electronic health records to conduct clinical data science. The six courses in this series are created to supplement the learner's existing programming and statistics skills by giving examples of specific problems, tools, and appropriate ways to interpret clinical data.

After completing this specialisation, you will be able to: 1) comprehend the different types and structures of data found in electronic health records; 2) apply fundamental informatics methodologies to clinical data; 3) interpret applied analyses in a way that is both clinically and scientifically sound; and 4) foresee potential obstacles to integrating informatics tools into challenging clinical settings. By completing real-world application projects with clinical data, you will prove that you have mastered these skills.

You will be ready to finish the Clinical Data Science Specialization after taking this course. You will discover how clinical data are produced, how they are formatted, and what ethical and legal constraints apply to them in this course. Even if you are a novice programmer, you will learn enough SQL and R programming skills to finish the Specialization. You will have access to a real clinical data set and a free, online data science computing environment while taking this course, both of which are hosted by our industry partner Google Cloud.After completing this course, you will be ready to begin your educational journey in clinical data science, learning how to use data generated by the healthcare system to enhance the health of patients in the future.


Describe how each type of clinical data are generated, specifically outlining who creates the data, when and why the data are generated.

  • Write SQL code to combine two or more tables using database joins.
  • Write R code to manipulate and tidy data including: selecting columns, filtering rows, and joining data sets.
  • Write markdown formatted text and combine with R code in RMarkdown documents.

Benefits of learning Clinical Data Science at Solve Tech Training Institute

  • Our experienced professionals are instructing this course.
  • Solve Tech provides you the facility to schedule the classes according to your availability.
  • Following each session, you'll receive the course material for your own future reference.
  • You will receive an internationally recognised certification after completing the course.


Focused Training
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Affordable Course Fee