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    Overview
    Selective: NDT 2249- Introduction to Healthcare Data Analytics (Dietetics with Nutrition & Nutrition)
    References
    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

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