Target Audience:
Healthcare analysts, clinical researchers, operations professionals, and early-career data scientists preparing for data roles in healthcare settings.
Course Objective:
Equip learners with practical, project-driven Python skills to work with healthcare data, automate workflows, build reports, and prepare for AI/ML implementation in real-world clinical and operational environments.
Module 1: Python Foundations for Data Analysis
Purpose:
Establish a foundational understanding of Python syntax and structures, with direct application to real-world healthcare tasks.
Key Topics:
Python variables, data types (strings, integers, lists, dictionaries)
Conditional statements and loops
Functions and modular code
File handling (read/write CSV, JSON)
Introduction to Jupyter Notebooks
Practice Tasks:
Build a script to calculate BMI using height and weight data
Loop through patient vitals and flag abnormal results
Load and inspect a .csv file of hospital visits
Write a function to compute medication dosage based on weight and age
Module 2: Healthcare Data Manipulation with Pandas and NumPy
Purpose:
Enable learners to clean, organize, and manipulate healthcare datasets using core data science libraries.
Key Topics:
Introduction to Pandas DataFrames and Series
Data ingestion (CSV, Excel, JSON)
Handling missing data, duplicates, and data types
Sorting, filtering, and conditional logic
Using NumPy for vectorized operations and calculations
Practice Tasks:
Clean a hospital EHR export with missing discharge dates and mixed data types
Group data by diagnosis and compute average treatment cost
Use NumPy to flag high-risk patients based on multiple conditions
Merge datasets (e.g., patient table with lab results)
Module 3: Healthcare Data Visualization and Reporting
Purpose:
Develop data storytelling skills through visual analysis, reporting, and dashboarding techniques using Matplotlib and Seaborn.
Key Topics:
Basic and advanced chart types (line, bar, pie, box, histogram)
Visualizing trends and distributions in clinical data
Using color and layout to highlight key insights
Exporting charts into reports (PDF, Excel)
Best practices in healthcare data reporting
Practice Tasks:
Create a line chart to track monthly patient admissions
Compare outcomes across treatment groups using box plots
Build a correlation heatmap of patient metrics
Generate a PDF summary report with embedded visualizations
Module 4: Automation and APIs in Healthcare
Purpose:
Introduce automation techniques and external data integration through scripting and API access.
Key Topics:
File and directory automation using os, shutil, and schedule
Email automation with attachments using smtplib
Accessing healthcare APIs (FHIR, CDC, WHO)
Parsing and processing JSON responses
Creating repeatable, scheduled data workflows
Practice Tasks:
Schedule a weekly script to back up daily patient logs
Query a COVID-19 API and extract statistics for reporting
Create a script to send KPIs via email each morning
Automate transformation and export of clinical trial datasets
Module 5: Interview Preparation and Introduction to AI/ML in Healthcare
Purpose:
Prepare learners for interviews and expose them to the fundamental principles of machine learning in a healthcare context.
Key Topics:
Common Python and Pandas interview questions and exercises
Technical case studies related to healthcare operations and outcomes
Introduction to classification and regression using Scikit-learn
Overview of healthcare KPIs, fairness, and data ethics
Mapping roles in healthcare analytics: Analyst, Data Scientist, ML Engineer
Practice Tasks:
Solve a case study on patient readmission predictors using Python
Build a basic logistic regression model to classify patient risk
Evaluate model accuracy, precision, and recall
Use the STAR method to prepare answers to technical and behavioral interview questions
