| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ~Get Your Files Here ! | |||
| 1 - Introduction | |||
| 1. Course Overview.mp4 | 18.7 MB | ||
| 1. Presentation - Python for Healthcare Data Analytics.pdf | 27 MB | ||
| 2 - Python Refresher | |||
| 3 - First steps with healthcare datasets | |||
| 10. First_steps_healthcare_datasets.ipynb.bin | 6.8 KB | ||
| 10. Loading dataset into Python.mp4 | 33.6 MB | ||
| 10. healthcare-dataset-stroke-data.csv | 309.5 KB | ||
| 11. Basic Exploratory Analysis.mp4 | 63.5 MB | ||
| 12. Exercise.mp4 | 6.3 MB | ||
| 4 - Data Cleaning and Preprocessing | |||
| 13. Data Cleaning and Preprocessing 1.mp4 | 59.1 MB | ||
| 13. Data_cleaning_preprocessing.ipynb.bin | 7 KB | ||
| 14. Data Cleaning and Preprocessing 2.mp4 | 46.5 MB | ||
| 5 - Exploratory Data Analysis for Healthcare | |||
| 15. EDA_healthcare.ipynb.bin | 8.7 KB | ||
| 15. Exploratory Data Analysis 1.mp4 | 67.9 MB | ||
| 16. Exploratory Data Analysis 2.mp4 | 138.7 MB | ||
| 17. Exercise.mp4 | 4.9 MB | ||
| 6 - Feature engineering and Building basic predictive models | |||
| 18. Building simple predictive models.mp4 | 78.2 MB | ||
| 18. Feature_engineering_Simple_Predictive_models.ipynb.bin | 13.1 KB | ||
| 7 - Capstone project | |||
| 19. Capstone.mp4 | 35.5 MB | ||
| 19. capstone_diabetes_end_to_end.ipynb.bin | 9.3 KB | ||
| 8. Navigating Google Colab Environment.mp4 | 17.2 MB | ||
| 9. Environment_python_refresher.ipynb.bin | 6.4 KB | ||
| 9. Python Refresher.mp4 | 80.6 MB | ||
| 2. Healthcare Data Types.mp4 | 17.6 MB | ||
| 3. Why this course Matters.mp4 | 8 MB | ||
| 4. Introduction to Healthcare Analytics.mp4 | 26 MB | ||
| 5. Healthcare Data Types 2.mp4 | 22.3 MB | ||
| 6. Python for Healthcare Analytics.mp4 | 14.2 MB | ||
| 7. Exercise.mp4 | 3.6 MB |
Python for Healthcare Data Analytics and Predictive Modeling
https://WebToolTip.com
Published 3/2026
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 2h 19m | Size: 769.72 MB
Use Python to load, clean, analyze, visualize healthcare data, and build basic predictive models.
What you'll learn
Use Python and Jupyter/Colab to load, clean, and explore real‑world healthcare datasets.
Perform descriptive statistics and create insightful visualizations to uncover trends in clinical and public health data.
Apply core analytics techniques (grouping, aggregation, time‑series analysis, correlation) with libraries like pandas, NumPy, and Matplotlib/Seaborn.
Build and evaluate basic predictive models (e.g., regression, classification) for common healthcare analytics tasks.
Work with common healthcare data formats (CSV, Excel, simple EHR‑style tables) while applying good practices for data quality and reproducibility.
Requirements
Basic computer literacy and ability to install software or use Google Colab.
No prior programming experience required; all necessary Python basics are taught in the course.
Interest in healthcare, public health, or clinical data and a willingness to work with real datasets.
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