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SELECTIVE: BPH2357(H) Introduction to Healthcare Data Analytics
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Selective: NDT 2249- Introduction to Healthcare Data Analytics (Dietetics with Nutrition & Nutrition)
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READING LIST
Introduction to Big Data and Data Science
What is Big Data? (5:49 minutes)
How is big data used? (11:07 minutes)
The Life of Data Science (by DataMind) (1:27 minutes)
Ten ways big data is different from small data (5:00 minutes)
The three facets of data science (5:50 minutes)
Data science without big data (6:48 minutes)
Big data without data science (4:52 minutes)
A Day in the Life of a Data Analyst (by Intermountain Healthcare) (2:56 minutes)
2. Big Data Analytics in Healthcare
How Big Data Could Transform The Health Care Industry (by HuffPost) (3:49 minutes)
Applications of Healthcare Analytics (9:05 minutes)
Healthcare Analytics, Health Informatics and Bioinformatics (9:32 minutes)
What Is Bioinformatics? (by Utrecht Bioinformatics Center) (2:17 minutes)
Data Science And Genetics (4:29 minutes)
Data science and cancer (5:05 minutes)
A Data Sharing Platform to Promote Precision Oncology: The Genomic Data Commons (by National cancer Institute, USA) (2:48 minutes)
NCI Cancer Research Data Commons – Harmonizing Research & Data Science for Better Clinical Outcomes (by National Cancer Institute, USA) (2:19 minutes)
Data science and epidemiology (5:45 minutes)
Using AI To Track The Coronavirus (by BBC) (5:26 minutes)
Data science for needs and outcomes assessments in healthcare (6:16 minutes)
Data ethics in healthcare, medicine, and public health (8:30 minutes)
Challenges with anonymity (5:31 minutes)
Challenges with confidentiality (3:47 minutes)
3. Healthcare Analytics Lifecycle
AI: From Data to Knowledge (2:15 minutes)
Decisions Making at Scale (9:02 minutes)
Types of data analytics (5:34 minutes)
Managing the Analytics Life Cycle for Decisions Making (5:28 minutes)
Big Data Analytics Life Cycle (9:48 minutes)
4. Asking the Right Question
Asking the Right Question (5:09 minutes)
Crafting better questions (3:55 minuites)
Setting SMART goals (9:31 minutes)
How to write your own SMART goals? (3:58 minutes)
5. Preparing Data for Analysis
Human-generated data (6:33 minutes)
Machine-generated data (3:06 minutes)
Structured and Unstructured Data (6:42 minutes)
Getting Started with Data (2:53 minutes)
Sourcing Existing Data (4:34 minutes)
Application Programming Interface (API) (4:11 minutes)
Data Scraping (2:02 minutes)
Collecting New Data (3:13 minutes)
Make Connections with Relational Databases (3:32 minutes)
Get data into warehouses using ETL (4:25 minutes)
NoSQL (5:05 minutes)
6. Data exploration and visualization
Importance of Exploratory Data Analysis (3:33 minutes)
Data Cleanup and Transformation (4:57 minutes)
Dealing with Missing Values (7:02 minutes)
Dealing with Outliers (2:53 minutes)
Adding and Removing Variables (4:56 minutes)
Common Graphs (8:33 minutes)
Online Assessment 1 (15%)
Online Assessment 1 (Topic 1 - 6) - 15%
7. Build Analysis - Predictive Analytics
Prediction Target (2:28 minutes)
Cohort Construction (4:30 minutes)
Feature Construction (2:40 minutes)
Feature Selection (1:44 minutes)
Predictive Model (1:09 minutes)
Performance Evaluation (3:26 minutes)
8. Build Analysis - Machine Learning (Supervised Learning)
Supervised Machine Learning
Build Analysis - Machine Learning (Supervised Learning)
9. Build Analysis - Machine Learning (Unsupervised Learning)
Unsupervised Learning
Build Analysis - Machine Learning (Unsupervised Learning)
10. Validate analysis
Validate analysis
Model Selection and Evaluation
Online Assessment 2 (15%)
Online Assessment 2 (Topic 7 - 10)
11. Case Study 1: Computational Phenotyping
Computational Phenotyping
Hands-on Workshop - Getting Started with Orange
Download Orange Data Mining Software
Case Study 1: Instructions
12. Case Study 2: Patient Similarity
Patient Similarity
Case Study 2 Instructions
The Cancer Genome Atlas (TCGA) Project Pancretic Cancer Dataset
Patient X
Case Study 2 Guidelines
13. Case Study 3: Building a Recommender System
Building a Recommender System
Case Study 3: Guidelines
Exercise Files
Installing Jupyter Notebook
14. Deploy and Evaluate Model
Deploy & Evaluate Model
15. Communicating Analytics Results
Communicating Analytics Results
Communicating Analytics Results
16. Data Governance and Ethics
What is Data Governance?
Data Governance and Ethics
Online Assessment 3 (15%)
Online Assessment 3 (Topic 14-16) - 15%
Class Test (55%)
Class Test (40 MCQs, 1 hour, 55%)
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SELECTIVE: BPH2357(H) Introduction to Healthcare Data Analytics
BPH2357
Coordinator:
YIH YIH KOK
Coordinator:
Chee Onn Leong
Coordinator:
Kingston Rajiah
Coordinator:
WAI KEAT YAM
