Data Science For Healthcare

Transform Data into Insights, Innovation, and Improved Healthcare

What you'll learn

Set up and navigate the Jupyter notebook environment.

Understand and apply Python basics for data science.

Acquire and import healthcare data from various sources.

Clean and preprocess data for analysis.

Perform exploratory data analysis (EDA) to uncover trends and patterns.

Apply data preprocessing techniques to prepare data for modeling.

Build and evaluate a basic machine learning model to predict health risk.

Requirements

Basic understanding of statistics and computer skills.

No prior programming experience required.

Description

This course is designed to introduce healthcare students and professionals to the fundamentals of data science using Python. It covers a wide range of topics from basic programming skills to advanced data analysis and machine learning techniques, all within the context of healthcare applications. Through interactive Jupyter notebooks, participants will learn how to analyze and interpret complex healthcare data, ultimately gaining insights that can inform clinical decisions, enhance patient care, and drive healthcare innovation.Target Audience• Healthcare students (medicine, pharmacy, nursing, public health, etc.)• Healthcare professionals (clinicians, researchers, administrators) looking toincorporate data science into their practicePrerequisites• Basic understanding of statistics• Basic computer skills• No prior programming experience requiredBy the end of this course, participants will be able to:• Set up and navigate the Jupyter notebook environment.• Understand and apply Python basics for data science.• Acquire and import healthcare data from various sources.• Clean and preprocess data for analysis.• Perform exploratory data analysis (EDA) to uncover trends and patterns. • Apply data preprocessing techniques to prepare data for modeling.• Build and evaluate a basic machine learning model to predict health risk.Course StructureThe course contains eight modules in total, covering topics from an introduction and setting up your coding environment to building and evaluating a machine learning model.Each module includes example coding notebooks, videos, quizzes, and supplementary notes to enhance your learning experience.

Overview

Section 1: Introduction

Lecture 1 Introduction to Data Science for Healthcare

Section 2: Python Basics for Data Science

Lecture 2 Module 2 Introduction

Lecture 3 Module 2-1: Variables, Data Types, and Operators

Lecture 4 Module 2-2 part I: Loops and Data Structures

Lecture 5 Module 2-2 part II: Loops and Data Structures

Lecture 6 Module 2-3 part I: More Lists, Methods, and Functions

Lecture 7 Module 2-3 part II: More Lists, Methods, and Functions

Lecture 8 Module 2-4 part I: Libraries and Object-Oriented Programming

Lecture 9 Module 2-4 part II: Libraries and Object-Oriented Programming

Section 3: Acquiring and Importing Data

Lecture 10 Module 3 Introduction

Lecture 11 Module 3 part I: Acquiring and Importing Data

Lecture 12 Module 3 part II: Acquiring and Importing Data

Section 4: Data Wrangling

Lecture 13 Module 4 Introduction

Lecture 14 Module 4 part I: Data Wrangling

Lecture 15 Module 4 part II: Data Wrangling

Section 5: Exploratory Data Analysis

Lecture 16 Module 5 Introduction

Lecture 17 Module 5 part I: Exploratory Data Analysis

Lecture 18 Module 5 part II: Exploratory Data Analysis

Section 6: Data Preprocessing

Lecture 19 Module 6 Introduction

Lecture 20 Module 6 part I: Data Preprocessing

Lecture 21 Module 6 part II: Data Preprocessing

Section 7: Machine Learning

Lecture 22 Module 7 Introduction

Lecture 23 Module 7 part I: Machine Learning

Lecture 24 Module 7 part II: Machine Learning

Lecture 25 Module 7 part III: Machine Learning

Section 8: Visualization and Presentation

Lecture 26 Module 8 Introduction

Lecture 27 Module 8 Notes

Healthcare students, such as pharmacy, medical, or nursing students interested in learning data science skills.,Healthcare professionals interested in learning data science skills.,Anyone interested in learning more about the intersection of healthcare and data science.