Curriculum
14 Sections
46 Lessons
6 Weeks
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Module 1: Fundamentals of Data Science
4
1.1
Data Collection and Sources in Healthcare
1.2
Data Types and Structures: Structured vs. Unstructured Data
1.3
Introduction to Statistics for Data Science
1.4
Basics of Data Cleaning, Transformation, and Preprocessing
Module 2: Core Data Science Applications in Healthcare
2
2.1
Classification Techniques for Diagnostics
2.2
Predictive Modeling for Disease Risk and Patient Outcomes
Module 3: Data Manipulation and Visualization with Python
4
3.1
Introduction to Python Libraries: NumPy, Pandas, Matplotlib, and Seaborn
3.2
Working with Pandas for Data Cleaning and Analysis
3.3
Visualizing Data Trends with Matplotlib and Seaborn
3.4
DIY Project: Analyzing Patient Data with Python
Module 4: Advanced Python for Healthcare Applications
4
4.1
Functions and Modules in Python
4.2
File Handling for Large Datasets
4.3
Introduction to Working with APIs for Healthcare Data Retrieval
4.4
Case Study: Fetching and Analyzing Healthcare Data from an API
Module 5: Core Concepts of Data Science
3
5.1
Data Science Workflow: From Collection to Insights
5.2
Exploratory Data Analysis (EDA) Identifying Trends and Patterns in Healthcare Data Tools for Summarizing Data
5.3
DIY Project: Conducting EDA on Hospital Readmissions Data
Module 6: Predictive Analytics in Healthcare
3
6.1
Overview of Predictive Analytics
6.2
Regression Models: Linear Regression for Outcome Prediction Multiple Regression Analysis
6.3
Case Study: Predicting Patient Recovery Time Using Regression Models
Module 7: Digital Transformation in Healthcare
4
7.1
Role of Data Science in Digitizing Healthcare Systems
7.2
Telemedicine and Remote Monitoring Applications
7.3
Health Informatics and Decision Support Systems
7.4
DIY Project: Designing a Simple Healthcare Operations Dashboard
Module 8: Introduction to Healthcare Data Ethics
4
8.1
Privacy and Security Challenges in Healthcare Data
8.2
Ethical Considerations in AI and Predictive Models
8.3
Regulations and Standards(HIPAA, GDPR, FDA Guidelines)
8.4
Case Study: Ethical Challenges in AI-Based Diagnostic
Module 9: Introduction to Data Visualization Techniques
4
9.1
Storytelling with Data in Healthcare
9.2
Advanced Chart Types: Heatmaps, Treemaps, and Boxplots
9.3
Best Practices for Creating Effective Visualizations
9.4
DIY Project: Building an Interactive Patient Data Dashboard
Module 10: Quantum Computing in Healthcare
4
10.1
Basics of Quantum Computing: Simplified Introduction
10.2
Applications in Drug Discovery and Genomic Analysis
10.3
Challenges and Future Potential in Healthcare
10.4
Discussion: How Quantum Could Revolutionize Medical Research
Module 11: Practical Applications of Data Science in Healthcare
1
11.1
End-to-End Healthcare Case Studies: Hospital Readmission Analysis Predicting Patient Length of Stay Disease Outbreak Prediction Models
Module 12: DIY Projects for Hands-On Learning
3
12.1
Building a Predictive Model for Diabetes Risk
12.2
Segmenting Patients for Population Health Management
12.3
Creating an EHR Data Dashboard
Module 13: Capstone Project
3
13.1
Goal: Develop a Data Science Solution for a Real Healthcare Challenge
13.2
Examples: Chronic Disease Management Dashboard Predictive Analytics for Surgery Outcomes
13.3
Peer Feedback and Evaluation
Module 14: Future Trends and Career Opportunities
3
14.1
Emerging Trends: AI, Quantum Computing, and Advanced Analytics
14.2
Career Pathways in Data Science for Healthcare Professionals
14.3
Resourcesfor Continuous Learning and Networking
PG Diploma in Data Science & Analytics for Healthcare Professionals
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