Data Science & Image Processing for Healthcare
- Created By raju2006
- Last Updated December 26th, 2023
- Overview
- Prerequisites
- Audience
- Audience
- Curriculum
Description:
Data science and digital image processing are becoming an increasingly integral part of health care. This course will expose you to many of the ways that data science is used to extract innovative and actionable insights from healthcare-related datasets and medical imaging.
Long Description:
Data science and digital image processing are becoming an increasingly integral part of health care. This course will expose you to many of the ways that data science is used to extract innovative and actionable insights from healthcare-related datasets and medical imaging. In this course, we will examine how predictive modeling is used to assess outcomes, needs and potential interventions. We will also explore medical image analysis which has become an inherent part of medical technology.
Course Code/Duration:
BDT47 / 3 Days
Learning Objectives:
After this course, you will be able to:
- Install Anaconda on a personal computer.
- Prepare and explore healthcare-related datasets using the primary tools for data science in Python (e.g., NumPy, Pandas, Matplotlib, Scikit-learn).
- Examine many of the unique qualities and challenges of healthcare data.
- Understand how data science is impacting medical diagnosis, prognosis and treatment.
- Use a data-science approach to evaluate and learn from healthcare data (e.g., behavioral, genomic, pharmacological).
- Use deep learning and TensorFlow to interpret and classify medical images.
- Perform feature extraction, segmentation and quantitative measurements of medical images.
- Understand the increasing importance of data science and image processing in healthcare.
- Basic Python Programming
- This course is designed for Healthcare professionals to get started with the domain of Machine Learning and Artificial Intelligence.
- This course is designed for Healthcare professionals to get started with the domain of Machine Learning and Artificial Intelligence.
Course Outline:
- Course Introduction
- Overview of Data Science in Healthcare
- Milestone 1: Install Anaconda/Work with Jupyter Notebooks
- The Data Science Process
- How Data Science is transforming the healthcare sector
- Essential Python Data Science Libraries
- Numpy
- Pandas
- Matplotlib
- Data Exploration
- Line Chart
- Scatterplot
- Pairplot
- Histogram
- Density Plot
- Boxplot
- Customizing Charts
- Milestone 2: Perform Exploratory Data Analysis of Healthcare Datasets
- Milestone 3: Use Scikit-learn to Apply Machine Learning to Healthcare Questions
- Introduction to Deep Learning for Medical Image Analysis
- Digital Image Processing
- Contrast and Brightness Correction Edge Detection
- Image Convolution
- Milestone 4: Use TensorFlow to Interpret and Classify Medical Images
- Conclusion: Next Steps
Structured Activity/Exercises/Case Studies:
- Milestone 1: Install Anaconda/Work with Jupyter Notebooks
- Milestone 2: Perform Exploratory Data Analysis of Healthcare Datasets
- Milestone 3: Use Scikit-learn to Apply Machine Learning to Healthcare Questions
- Milestone 4: Use TensorFlow to Interpret and Classify Medical Images
Training material provided:
Yes (Digital format)
