Biostatistics Fundamentals Using Python

Learn how easy it is to use Python to do your biostatistical analysis

What you'll learn
After completing this course, students will be able to use Python to do their own biostatistical analysis
Requirements
Students should have access to a computer with an internet connection. A basic understanding of statistics is assumed.
Description
This course empowers you to do your own biostatistical analysis.  Whether you are a healthcare professional, scientist, or just someone interested in supercharging their research career, the time to learn how to use a modern computer language to do you own analysis, has arrived.
Python is becoming the de facto standard in data analysis.  It is a free to use, powerful programming language.  With the minimum of effort, you will soon be able to do all you own analysis, create beautiful plots, and deliver your reports or publish your research with confidence and pride.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Motivation

Lecture 3 Why choose python?

Section 2: Installing the software

Lecture 4 Installing the software

Lecture 5 Installing python

Lecture 6 Jupyter notebook

Lecture 7 Shutting down Jupyter

Lecture 8 Introducing Google Colaboratory

Lecture 9 A sneak peek at a completed notebook

Section 3: Simple arithmetic

Lecture 10 Doing simple arithmetic

Section 4: Collections

Lecture 11 Collections

Lecture 12 Collections 01 - Python lists

Lecture 13 Collections 02 - Ranges

Lecture 14 Collections 03 - Dictionaries

Section 5: Working with data

Lecture 15 Working with data

Lecture 16 Working with data 01 - Importing external data

Lecture 17 Working with data 02 - Accessing the imported data

Lecture 18 Creating simulated, random data to play with

Lecture 19 Working with data 03 - Creating random data

Lecture 20 Working with data 04 - Creating a DataFrame with simulated random data

Lecture 21 Working with data 05 - Cleaning up data

Lecture 22 Working with data 06 - More cleaning up

Section 6: Descriptive statistics

Lecture 23 Descriptive statistics

Lecture 24 Descriptive statistics 01 - Measure of central tendency

Lecture 25 Descriptive statistics 02 - Measures of dispersion

Section 7: Data visualization

Lecture 26 Data visualization

Lecture 27 Data visualization 01 - Scatter plots

Lecture 28 Data visualization 02 - Scatter plots continued

Lecture 29 Data visualization 03 - Box plots

Lecture 30 Data visualization 04 - Histograms

Lecture 31 Data visualization 05 - Dot plots

Lecture 32 Data visualization 06 - Bar charts

Lecture 33 Data visualization - Introduction to Plotly Express

Lecture 34 Data visualization - Plotly Express Part 1

Lecture 35 Data visualization - Plotly Express Part 2

Section 8: Assumptions for the use of parametric tests

Lecture 36 Assumptions for the use of parametric tests

Lecture 37 Assumptions for the use of parametric tests 01 - Visual tests

Lecture 38 Assumptions for the use of parametric tests 02

Lecture 39 Assumptions for the use of parametric tests 03 - Homogeneity of variance

Lecture 40 Assumptions for the use of parametric tests 04 - Outliers

Section 9: Correlation and linear regression

Lecture 41 Correlation

Lecture 42 Correlations 01 - Univariate correlation

Lecture 43 Correlations 02 - Multivariate correlation

Lecture 44 Linear regression 01

Lecture 45 Linear regression 02

Lecture 46 Linear regression 03

Section 10: Comparing means

Lecture 47 Comparing means

Lecture 48 Comparing means

Section 11: Comparing categorical variables

Lecture 49 Comparing categorical variables

Lecture 50 Comparing means

Section 12: Logistic regression

Lecture 51 Logistic regression 01

Lecture 52 Logistic regression 02

Lecture 53 Logistic regression 03

Lecture 54 Logistic regression 04

Lecture 55 Logistic regression 05

Section 13: Research projects

Lecture 56 Full projects

Lecture 57 Getting started with a case-control study

Lecture 58 Descriptive statistics for this case-control study

Lecture 59 Inferential statistics for this case-control study

Any scientist, healthcare worker, or person interested in doing their own biostatistics.