Health Care Data Analysis

Course Code: HSEC 174

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Course Sector:

Health, Safety and Environment

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Course Dates and Locations

Choose a date and location to book your seat

No.

Date

Days

Location

Fees

Enrollment

11 - 15 Aug 2025

5 Days

Singapore,SG

$4,950

22 - 26 Dec 2025

5 Days

Dubai, UAE

$4,250

Introduction

Training course introducion / brief

The Healthcare Data Analysis training program provides participants with comprehensive knowledge and practical skills in analyzing healthcare data to support informed decision-making and improve patient outcomes. Throughout the course, participants will learn the essential concepts and techniques of healthcare data analysis, starting from data collection and preprocessing to advanced predictive modeling and machine learning. The training begins with an introduction to healthcare data sources and basic data analysis techniques, gradually progressing to more advanced topics such as predictive modeling, machine learning, and healthcare analytics tools. Participants will engage in hands-on exercises and real-world case studies to reinforce their learning and develop proficiency in applying data analysis techniques to healthcare datasets

Understand the fundamentals of healthcare data analysis, including data sources, collection methods, and preprocessing techniques.
Gain proficiency in exploratory data analysis (EDA) to identify patterns, trends, and outliers in healthcare datasets.
Develop skills in predictive modeling and machine learning for healthcare applications, such as disease diagnosis and patient outcome prediction.
Learn how to leverage healthcare analytics tools and platforms to create interactive dashboards and reports for data-driven decision-making.
Explore advanced topics in healthcare data analysis, including survival analysis, natural language processing (NLP), and ethical considerations.

This training is tailored for healthcare professionals and analysts aiming to enhance their proficiency in data analysis techniques specific to the healthcare industry.

Introduction to Healthcare Data Analysis

Understanding the importance of data analysis in healthcare decision-making
Overview of healthcare data sources, including electronic health records (EHR), claims data, and clinical trials data
Introduction to data collection, storage, and management practices in healthcare
Basics of data preprocessing, cleaning, and quality assurance techniques
Exploring healthcare datasets and basic data manipulation in statistical software

Exploratory Data Analysis in Healthcare

Exploring descriptive statistics and data visualization techniques for healthcare data
Identifying patterns, trends, and outliers in healthcare datasets
Introduction to statistical hypothesis testing and its applications in healthcare research
Hands-on exercises: Conducting exploratory data analysis on real-world healthcare datasets using statistical software

Predictive Modeling and Machine Learning in Healthcare

Introduction to predictive modeling concepts and algorithms in healthcare
Building predictive models for healthcare outcomes, such as disease diagnosis, patient readmission, and treatment response prediction
Evaluating model performance and interpreting model results
Introduction to machine learning techniques, including decision trees, logistic regression, and ensemble methods
Building and evaluating predictive models using healthcare datasets and machine learning libraries in Python or R

Healthcare Analytics and Business Intelligence

Understanding the role of analytics and business intelligence in healthcare decision support
Overview of healthcare analytics tools and platforms, such as Tableau, Power BI, and Qlik
Creating interactive dashboards and reports to communicate healthcare insights effectively
Introduction to healthcare performance metrics and key performance indicators (KPIs)
Building healthcare dashboards and reports using analytics software

Advanced Topics in Healthcare Data Analysis

Advanced statistical analysis techniques for healthcare research, including survival analysis, longitudinal data analysis, and causal inference methods
Introduction to natural language processing (NLP) techniques for analyzing unstructured healthcare data, such as clinical notes and medical literature
Ethical considerations and regulatory compliance in healthcare data analysis, including patient privacy and data security

Boost certificate of completion

BOOST's Professional Attendance Certificate “BPAC” is always given to the delegates after completing the training course, and depends on their attendance of the program at a rate of no less than 80%, besides their active participation and engagement during the program sessions.

ENDORSED EDUCATION PROVIDER

Flexible deadlines

Customized dates accordance to your schedule

Shareable Certificate

Earn certificate upon completion

COURSE METHODOLOGY

Our Training programs are implemented by combining the participants' academic knowledge and practical practice (30% theoretical / 70% practical activities).

At The end of the training program, Participants are involved in practical workshop to show their skills in applying what they were trained for. A detailed report is submitted to each participant and the training department in the organization on the results of the participant's performance and the return on training. Our programs focus on exercises, case studies, and individual and group presentations.

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TRAINING COURSE: Health Care Data Analysis