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Fundamentals of Data Science in Healthcare
Course
Fundamentals of Data Science in Healthcare
Learn how healthcare data is created and prepared. Build a strong foundation for clinical and operational analytics.
Flexible Schedule
Intermediate Level
Mentor Support
Estimated Time
2 hours/week
This course equips you with foundational data science skills to work confidently with real-world healthcare data. You will understand how healthcare data is generated, structured, standardized, and prepared for analytics across clinical, operational, and administrative settings so you can make sense of the data you encounter on the job.
You will explore major healthcare data sources such as electronic health records, claims, labs, and registries, and learn how to handle common challenges including missing data, inconsistent formats, fragmented systems, and complex timelines. The course introduces essential healthcare standards such as ICD-10, SNOMED CT, HL7, and FHIR, enabling you to work with interoperable data across systems.
Through hands-on labs, you will clean raw clinical datasets, assess data quality, engineer analytical features, and apply HIPAA-aligned de-identification techniques. You will also integrate data from multiple sources to create model-ready datasets suitable for downstream analytics and machine learning.
Unlike generic data science courses, this course is designed specifically for healthcare. It prepares you to handle healthcare-specific data, standards, privacy requirements, and real-world constraints that are critical for performing effectively in healthcare analytics roles.
This course comprises four purposely designed modules that take you on a carefully defined learning path.
It is a self-paced course, which means it is not run to a fixed schedule with regard to completing modules or submitting assignments. To give you an idea of how long the course takes to complete, it is anticipated that if you work 2 hours per week, you will complete the course in 4 weeks. However, as long as the course is completed before the end date, you can work at your own pace.
The materials for each module will become available when you start the particular module. Methods of learning and assessment will include videos, reading material, online exams questions, and a final peer review assignment.
Once you have successfully completed the course, you will earn your Certificate of Completion.
By the end of this course, you will be able to:
- Perform data preprocessing.
- Understand health informatics.
- Apply machine learning techniques.
- Implement HIPAA-compliant data de-identification.
- Integrate and harmonize healthcare data.
- Aspiring data scientists seeking to work with real-world healthcare data
- Analysts and engineers transitioning from non-healthcare domains into healthcare analytics
- Health IT and informatics professionals seeking to understand data science applications in clinical and operational settings
- Individuals with basic statistics and spreadsheet skills who want hands-on experience using Python and Jupyter Notebook to preprocess healthcare datasets
- Basic knowledge of Python, statistics, and data analysis concepts
- Familiarity with machine learning and healthcare terminology
Course Outline
Video: Specialization Overview (2:57)
Video: Course Introduction (3:29)
Reading: Course Overview
Reading: Grading Scheme
Reading: How to Make the Most of This Course
Reading: Module Description and Learning Objectives
Activity: What Difference Do You Want to Make in Healthcare?
Video: How Healthcare Processes Generate Data (4:54)
Video: How Different Stakeholders Use Healthcare Data (3:52)
Reading: Case Example: Using Multi-Source Data to Analyze Disease Progression
Practice Quiz: Healthcare Data Sources
Video: Structures and Patterns in Healthcare Data (4:25)
Video: Common Data Quality Challenges in Healthcare (4:34)
Reading: How Data Quality Shapes Clinical Decision Support
Lab: Exploring Raw EHR Data for Structure and Quality Issues
Practice Quiz: Structure, Format and Quality of Healthcare Data
Video: Healthcare Data Journey from Patient Intake to Analytics (4:16)
Reading: Healthcare System Integration Points
Practice Quiz: Healthcare Data Flow Across Systems
Module Summary: Healthcare Data Landscape and Ecosystem
Module 1: Graded Quiz: Healthcare Data Landscape and Ecosystem
Reading: Module Description and Learning Objectives
Video: Clinical Vocabulary Standards (4:41)
Video: How to Read and Apply Clinical Codes to Patient Cases (5:15)
Reading: Common Misclassification Problems in Healthcare Codes
Activity: How Coding Decisions Shape Healthcare Analytics
Practice Quiz: Clinical Coding Systems
Video: Healthcare Data Exchange with HL7 v2 and FHIR (5:05)
Reading: Interoperability Problems and Their Impact on Analytics
Lab: Interpreting and Validating FHIR Resources
Practice Quiz: HL7, FHIR and Interoperability
Video: Harmonizing Healthcare Data for Analytics (4:39)
Reading: Common Pitfalls in Healthcare Data Integration
Lab: Harmonizing Multi-Source Healthcare Data
Activity: Spotting Data Integration Issues
Practice Quiz: Data Integration and Harmonization Across Systems
Module Summary: Healthcare Data Standards and Interoperability
Module 2: Graded Quiz: Healthcare Data Standards and Interoperability
Reading: Module Description and Learning Objectives
Video: Cleaning Raw Healthcare Data (5:08)
Video: HIPAA De-Identification Techniques (5:05)
Reading: Common Preprocessing Workflows in Healthcare Analytics
Lab: Cleaning Clinical Datasets and Applying HIPAA De-Identification
Practice Quiz: Cleaning and Preparing Healthcare Data
Video: Making Sense of Irregular Time-Series in EHR Data (4:41)
Reading: Building Patient Timelines for Analytics
Activity: Interpreting Patient Timelines
Practice Quiz: Temporal Alignment and Longitudinal Healthcare Data
Video: Creating Derived Features from Clinical Events (4:45)
Video: Transforming Healthcare Data into Model-Ready Features (5:10)
Lab: Feature Engineering for Clinical Events, Labs and Encounters
Practice Quiz: Feature Engineering for Healthcare Analytics
Module Summary: Preprocessing and Preparing Healthcare Data for Modeling
Module 3: Graded Quiz: Preprocessing and Preparing Healthcare Data for Modeling
Reading: Final Project Overview
Project: Building a Healthcare Analytics Dataset from Raw Multi-Source Data
Reading: Comparing Your Work
Video: Course Summary (4:49)
Reading: Course Glossary
Final Exam: Fundamentals of Data Science in Healthcare
Reading: Congratulations and Next Steps
Reading: Team and Acknowledgments
Why Learn with SkillUp Online?
We believe every learner is an individual and every course is an opportunity to build job-ready skills. Through our human-centered approach to learning, we will empower you to fulfil your professional and personal goals and enjoy career success.
Reskilling into tech? We’ll support you.
Upskilling for promotion? We’ll help you.
Cross-skilling for your career? We’ll guide you.
Personalized Mentoring & Support
1-on-1 mentoring, live classes, webinars, weekly feedback, peer discussion, and much more.
Practical Experience
Hands-on labs and projects tackling real-world challenges. Great for your resumé and LinkedIn profile.
Best-in-Class Course Content
Designed by the industry for the industry so you can build job-ready skills.
Job-Ready Skills Focus
Competency building and global certifications employers are actively looking for.
FAQs
The course introduces you to data science in healthcare, helping you understand how raw clinical data becomes usable insights. You will explore real-world workflows, analytics basics, and practical applications across healthcare systems.
This course is ideal for beginners, healthcare professionals, and aspiring analysts. No deep coding needed, just curiosity to explore medical data science and how data improves patient care and decisions.
Yes, the course focuses on real-world challenges like missing values, inconsistent formats, and time-based data issues. You will learn how to clean, align, and structure data for accurate analysis.
You will work with EHRs, claims data, lab records, and registries, the key healthcare data sources used in real systems. It builds a strong foundation for handling diverse and complex datasets.
Yes, you will learn essential data processing skills like cleaning data, handling missing values, and preparing structured datasets for analysis. These skills are critical for professional growth in any healthcare data role.
This course is tailored specifically for healthcare. It focuses on domain-specific challenges, healthcare data sources, compliance needs, and standards, giving you targeted skills for medical data science rather than generic analytics.
Absolutely! You will understand HIPAA compliance basics and learn how to handle sensitive healthcare datasets securely using privacy-preserving and de-identification techniques.
Yes, the course introduces key healthcare data standards like ICD-10, SNOMED CT, and HL7. You will understand how these standards enable interoperability and ensure consistent, structured data across healthcare systems.
Absolutely! You will work on practical exercises using real healthcare datasets. These hands-on tasks help you apply the fundamentals of healthcare data science concepts like data cleaning, feature engineering, and preparation for analytics in real-world scenarios.
It builds practical skills used in healthcare analytics roles, such as working with fragmented data, healthcare datasets, improving data quality, and preparing datasets for decision-making, reporting, and machine learning applications.
Type of certificate
Certificate of Completion
About this course
04 Modules
05 Skills
Includes
Discussion Space
14 Videos
05 Hands-on labs
09 Practice quizzes
03 Graded quizzes
05 Activities
01 Final project
01 Final exam
Create
Healthcare Analytics Dataset from Raw Multi-Source Data
Exercises to explore
Python
Jupyter Notebook
Google Colab
This course has been created by
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