Health Data Analytics

Course offered by: Prof. Dr. Thomas Spittler
Offering University: TH Deggendorf
Course Language: English
Subject Area: Health Care / Health Management Free of Charge
Average Workload: 15 Hours Free of Charge Enrol
Picture Credits: Benedikt Krieger / Vyond

What awaits you in this course?

Technology can optimise the delivery of healthcare and drive down costs while improving patient outcomes however, as the volume of data keeps growing, it’s becoming critical to understand its value. This makes data an essential element of digital transformation.  Therefore, data analytics in healthcare assembles activities to analyse the collected data from various areas within the healthcare sector, such as clinical data, claims and cost data, pharmaceutical data, and sentiment data. This course aims to provide knowledge and insights into the objectives of the advanced analytical methods focused on healthcare. This includes the prediction models, statistical analysis, and visualisation methods. Integrating the associated challenges is essential for improving the predictive power of the data will also be discussed. These aspects are presented from a deep background knowledge up to the practical application and the benefits from the emerging technologies. The individual and practical focus towards data science and data analytics are further ensured by case studies.

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What can you learn in this course?

After completing this course, students will have achieved the skills needed to acquire the following:

  • Analyse the various types and sources of healthcare data, including clinical, operational, claims, and patient-generated data.
  • Assess the quality of healthcare data and make appropriate interpretations of meaning according to data sources and intended uses.
  • Compare and contrast commonly used data models and algorithms within the healthcare system.
  • Enumerate the associated challenges and ethical considerations of healthcare data analytics.


Unit 1: Basics of Data Analytics

  • Lesson 1: Introduction to Unit 1
  • Lesson 2: Data and Information
  • Lesson 3: Analytics and Decision Making in Healthcare
  • Lesson 4: Data Analytics Types
  • Lesson 5: Data Analytics Lifecycle
  • Lesson 6: Testing Learning Objectives

Unit 2: Data Analytics Algorithms

  • Lesson 1: Introduction to Unit 2
  • Lesson 2: Classification
  • Lesson 3: Datasets with Regression, Correlation, and Clustering
  • Lesson 4: Forecasting
  • Lesson 5: Testing Learning Objectives

Unit 3: Data Visualization with Examples

  • Lesson 1: Introduction to Unit 3
  • Lesson 2: Presentation and Visualization of Information
  • Lesson 3: Information Infographics
  • Lesson 4: Dashboards
  • Lesson 5: Testing Learning Objectives

Unit 4: Case Study: Challenges and Benefits for Health Data Analytics Applications with Ethical Consideration

  • Lesson 1: Introduction to Unit 4
  • Lesson 2: Improving Clinical Decision Making with Artificial Intelligence
  • Lesson 3: The Application of Tracking Gadgets For Wellness and Health
  • Lesson 4: Summary

Unit 5: Future Trends

  • Lesson 1: Introduction to Unit 5 
  • Lesson 2: From Data to Artificial Intelligence
  • Lesson 3: IoT Analytics (The Future with Smart Analytics)
  • Lesson 4: Cloud Computing
  • Lesson 5: Testing Learning Objectives
  • Lesson 6: Course Ending

Course offered by

Prof. Dr. - Ing. Thomas Spittler

Vice Dean at Deggendorf Institute of Technology, 
Faculty of European Campus Rottal-Inn, Pfarrkirchen, Germany

Head of the Health Informatics Program

Course Manager

Further authors

Helana Lutfi, M.Sc.

Research Associate at Deggendorf Institute of Technology,
Faculty European Campus Rottal-Inn, Pfarrkirchen, Germany

Target group

This course is designed for participants who are directly or indirectly involved in the healthcare sector. It provides solid knowledge for analysts, data and decision scientists, academic researchers, or future health informaticians.

Confirmation of participation

In this course you can be awarded a certificate of participation. Please go through the entire course and answer more than 80% of all tests and questions correctly.


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Analytics, Big Data, Benefits, Challenges, Healthcare, Methods