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Statistical Analysis and Data Modeling in Healthcare
Course
Statistical Analysis and Data Modeling in Healthcare
Build advanced healthcare analytics skills using statistics, predictive modeling, and Python to analyze real clinical data & support decision-making.
Flexible Schedule
Intermediate Level
Mentor Support
Estimated Time
2-3 hours/week
Advance your career in healthcare data analytics by mastering the statistical and predictive modeling techniques used across clinical, operational, and population health settings.
In this hands-on course, youll learn how to analyze real-world healthcare datasets using descriptive statistics, hypothesis testing, regression analysis, and machine learning. Through interactive labs using Python and Jupyter Notebook in a Google Colab environment, youll compute key metrics, evaluate clinical groups, build predictive models, and interpret results with confidence.
Designed for healthcare professionals, data analysts, and IT specialists, this course focuses on practical, industry-relevant skills. Youll discover how to assess treatment effectiveness, explore associations among clinical variables, and generate predictions that support evidence-based clinical decision-making. The course also emphasizes ethical data practices, model validation, fairness, and the unique challenges of working with healthcare data.
By the end of the course, you will be able to perform end-to-end healthcare data analysis, from data exploration and statistical testing to predictive modeling and interpretation. Youll develop job-ready skills in healthcare analytics, statistical modeling, clinical data interpretation, and machine learning for healthcare, preparing you for roles such as healthcare data analyst, clinical data manager, or quality improvement specialist.
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 2-3 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:
- Understand clinical assessment and clinical data management.
- Apply data analysis in healthcare contexts.
- Use decision tree learning and random forest algorithms.
- Apply machine learning and supervised learning methods.
- Perform predictive analytics for healthcare insights.
- Understand probability and statistical concepts.
- Conduct regression and statistical analysis.
- Perform hypothesis testing.
- Apply health informatics and healthcare ethics principles.
- Healthcare and public health professionals
- Data/BI professionals moving into healthcare analytics
- Healthcare IT and clinical data staff
- Learners with basic data and statistics knowledge
Basic knowledge of Python is required.
Course Outline
Video: Course Introduction (3:30)
Reading: Course Overview
Reading: Learning Objectives and Syllabus
Reading: How to Make the Most of This Course
Video: Specialization Overview (4:35)
Reading: Grading Scheme
Video: Meet Your Instructor (6:12)
Module Introduction and Learning Objectives
Activity: How Ready Are You?
Video: Measures of Central Tendency (3:54)
Video: Selecting the Right Measure of Center (3:46)
Video: Variability in Clinical Measurements (5:07)
Reading: Clinical Implications of Variability and Homogeneity
Activity: Reveal the Hidden Story in Patient Data
Video: Common Distribution Types in Healthcare (4:29)
Video: Impact of Data Distribution on Clinical Decisions (3:53)
Reading: Integrating Descriptive Statistics for Healthcare Decision-Making
Lab: Analyzing Patient Vital Signs with Descriptive Statistics
Module Summary: Descriptive Statistics in Healthcare
Practice Quiz: Descriptive Statistics in Healthcare
Graded Quiz: Descriptive Statistics in Healthcare
Module Introduction and Learning Objectives
Video: Null and Alternative Hypotheses in Clinical Research (4:21)
Video: P-Values and Confidence Intervals in Clinical Decisions (6:11)
Reading: Key Concepts of Clinical Hypothesis Testing
Activity: From Question to Evidence in Clinical Analytics
Video: T-Tests and ANOVA for Healthcare Outcomes (4:32)
Video: Nonparametric Tests for Skewed Clinical Data (5:04)
Reading: Comparing Parametric and Nonparametric Methods
Activity: Choosing the Right Test for Clinical Comparisons
Video: Chi-Square Tests for Categorical Clinical Variables (3:50)
Video: Correlation Analysis in Healthcare (4:23)
Reading: Association Tests in Healthcare Research
Lab: Evaluating Treatment Effectiveness with Hypothesis Testing
Lab: Comparing Multiple Clinical Groups with ANOVA
Module Summary: Hypothesis Testing for Clinical Data
Practice Quiz: Hypothesis Testing for Clinical Data
Graded Quiz: Hypothesis Testing for Clinical Data
Module Introduction and Learning Objectives
Video: Linear Regression for Clinical Metrics (5:05)
Video: Model Diagnostics and Assumption Checking (4:00)
Video: Logistic Regression for Binary Clinical Outcomes (4:34)
Reading: Odds Ratios and ROC Metrics for Clinical Models
Activity: Predicting Clinical Risk: From Logistic Regression to Model Evaluation
Video: Supervised Learning Methods for Healthcare (4:43)
Video: Model Validation, Bias, and Deployment Challenges (4:42)
Reading: Fundamentals of Machine Learning in Healthcare Analytics
Lab: Predicting Readmission Risk with Logistic Regression
Lab: Building Predictive Models with Decision Trees and Random Forests
Module Summary: Regression Analysis and Predictive Modeling
Practice Quiz: Regression Analysis and Predictive Modeling
Graded Quiz: Regression Analysis and Predictive Modeling
Module Introduction and Learning Objectives
Reading: Final Project Overview
Project: Emergency Department Analytics: From Statistics to Predictive Models
Video: Course Summary (4:03)
Course Glossary: Statistical Analysis and Data Modeling in Healthcare
Final Exam: Statistical Analysis and Data Modeling in Healthcare
Reading: Comparing Your Work
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
A basic understanding of healthcare operations, spreadsheets, and introductory Python is recommended. Since this is an intermediate-level statistical analysis course, familiarity with basic math and statistics will help you grasp concepts more effectively.
You will learn descriptive statistics, inferential statistics, and how to apply statistical models to healthcare data. The course also introduces data modeling techniques to support clinical decisions and operational improvements.
Yes, this healthcare data analytics training is highly relevant for professionals in hospitals or clinics. It helps you apply statistical analysis and data modeling in real-world healthcare settings to improve decision-making and patient outcomes.
The course includes concepts like hypothesis testing, correlation analysis, and basic regression modeling. It focuses on practical application rather than heavy coding, making it manageable for learners with foundational knowledge.
Yes, you will work with real-world or simulated datasets for healthcare. This helps you understand healthcare data handling, including cleaning, analyzing, and interpreting data to derive meaningful insights.
You will gain skills in statistical analysis, data modeling, and visualization. This includes working with descriptive and inferential statistics, building statistical models, and communicating insights effectively to healthcare stakeholders.
The course requires around 2 to 3 hours per week. It is designed to help you consistently build skills in statistical analysis and data modeling without overwhelming your existing schedule.
The course is designed for 4 weeks, allowing you to gradually build from foundational concepts to build job-ready healthcare analytics skills by combining data preparation, analysis, and visualization for clinical and business decisions.
Yes, it can be, with a basic understanding of healthcare operations, spreadsheets, introductory Python, and fundamental math and statistics concepts. The course helps bridge data analytics skills with healthcare applications, making it easier to work with datasets for healthcare and industry-specific insights.
You can explore roles like healthcare data analyst, or health informatics specialist, clinical data manager, quality improvement specialist, and other analytical-based roles in hospitals.
Yes, the course covers essential aspects of healthcare data handling, including privacy, security, and regulatory practices. This ensures you manage sensitive datasets for healthcare responsibly and ethically.
This course is tailored for healthcare, focusing on statistical analysis, hypothesis testing, and data modeling in clinical settings. It connects theory with real healthcare applications, making the learning more relevant and practical.
Type of certificate
Certificate of Completion
About this course
04 Modules
09 Skills
Includes
Discussion Space
19 Videos
05 Hands-on labs
03 Practice quizzes
03 Graded quizzes
01 Project
01 Final exam
Exercises to explore
Jupyter Notebook
Google Colab
This course has been created by
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