Python

Introduction to Python

1. Getting Started
   • Check Your Python Installation
2. Data Types
3. Errors
4. Maths and Numbers
5. Strings
   • f-Strings
6. Booleans and If Statements
7. Lists, Sets and Tuples
8. Dictionaries
9. Arrays
10. Loops
11. Functions
12. Classes

Advanced Python

1. Lists-of-Lists and List Comprehensions
2. Dictionaries-of-Dictionaries and Dictionary Comprehensions
3. Data Frames:
   • Introduction
   • Searching, Finding, Filtering
   • Calculations
   • Multiple Levels
4. Date and Time:
   • The time module
   • The datetime module
5. Args and Kwargs
6. Parsing Command-Line Arguments
7. Error Handling
8. Package Management and Virtual Environments

Working with Data

1. Example Datasets
   • From scikit-learn
   • From Statsmodels
2. File IO: Create, Export, Import and Convert Data
   • Export Tables to Word
3. Data Frames:
   • Merging
   • Grouping and Aggregating
   • Pivot Tables
4. Drop Empty Columns from Pandas Data Frames
5. Rounding Off
6. Using VLOOKUP for Unblinding
7. Clinical Study Design

Graphs (using Matplotlib)

1. Image Size
2. Symbols, Line Styles and Colours
   • Colours that Look Good in Figures
3. Working with Time-Based Data
4. Graphing using the ‘state machine interface’ (aka the pyplot style) - This is quicker than the object-orientated style for simple plots but it gives you less control:
   • Line Plots
     • Receiver Operating Characteristic (ROC) Curves
   • Scatter Plots
     • Bland-Altman Plots
   • Box Plots (aka box-and-whisker plots):
     • One Group of Data
     • Multiple Groups of Data
   • Bar Plots:
     • One Group of Data
     • Multiple Groups of Data
   • Histograms
   • Pie Charts
5. Graphing using axes objects (aka the object-orientated style) - This is more complicated than the pyplot style but it gives you more control:
   • Line Plots
     • Receiver Operating Characteristic (ROC) Curves
   • Scatter Plots
     • Bland-Altman Plots
   • Box Plots (aka box-and-whisker plots):
     • One Group of Data
       • Significance Bars
     • Multiple Groups of Data
   • Bar Plots:
     • One Group of Data
     • Multiple Groups of Data
   • Histograms
   • Pie Charts
6. Graphing directly from Pandas data frames - This is essentially the same as the above methods because Pandas uses Matplotlib in the background:
   • Line, Scatter & Box Plots, Histograms, Pie Charts and Sub-Plots
   • Bar Plots
7. Axes and Labels:
   • LaTeX in Labels
   • Changing the Font

Mathematics

1. Curve Fitting:
   • Linear Functions:
     • Comparison of Methods
     • Linear Regression
   • Exponential Functions
   • Logarithmic Functions
2. Combinations and Permutations

Statistics

1. Descriptive Statistics
2. Confidence Intervals
3. Effect Size
4. Sample Size Estimation
5. Outlier Rejection
6. Distributions:
   • Uniform Distributions
   • Binomial Distributions
   • Normal Distributions
     • Tests for Normality
   • Student’s t-Distributions
7. Hypothesis Testing:
   • Regression:
     • Constant Regression
     • Simple Linear Regression
     • Multiple Linear Regression
     • Quadratic Regression
     • Passing–Bablok Regression
   • t-Tests:
     • One-Sample t-Test
     • Unpaired Two-Sample t-Test
     • Two One-Sided t-Tests (TOST):
       • Unpaired
       • Paired
   • Z-Tests:
     • One-Sample Z-Test for a Proportion
     • Two-Sample Z-Test for a Proportion
   • Mann-Whitney U Test
     • Comparison of Mann-Whitney Methods
   • ANOVA
     • Tukey’s Range Test
   • Binomial Test
   • Chi-Squared Tests
8. Agreement - Including accuracy, precision (repeatability and reproducibility), variability and equivalence:
   • Cohen’s Kappa
   • Repeatability Coefficient
   • Coefficient of Variation
   • Correlation Coefficients:
     • Pearson Correlation Coefficient
     • Spearman’s Rank Correlation Coefficient
     • Concordance Correlation Coefficient (CCC)
     • Intraclass Correlation Coefficient (ICC)
   • Bland-Altman Analysis:
     • Traditional Bland-Altman Analysis (native space)
       • Sample Size Calculations
     • Giavarina Analysis (percentage space)
     • Euser Analysis (log 10 space)
9. Risk:
   • Odds Ratio
   • Hazard Ratio

Machine Learning

1. Supervised Machine Learning Models:
   • Regression:
     • Simple Linear Regression
     • Multiple Linear Regression
     • Logistic Regression
     • k-Nearest Neighbours Regression
   • Classification:
     • k-Nearest Neighbours Classifier
     • Decision Trees
     • Support Vector Machines

Utilities

1. Convert PDFs to Images
2. Extract Text From PDFs
3. Sunrise and Sunset Times
4. Password Management
5. Time a Script
6. Creating a Package
7. Testing Your Code
8. Coordinate Precision
9. Files and Folders
   • The os and pathlib Modules
   • Path Nomenclature
   • Move and Rename Files
   • Cut and Copy Files
   • Cleaning Names and Paths
   • Pretty-Printing Folder Structures

Image Analysis

1. Working With Images
   • Working With DICOMs
2. Filtering
3. Posterise
4. Segmentation via Selecting by Colour
5. Calculating Overlap and the Dice Coefficient
6. Compare Two Images
7. Taking Screenshots
8. Click to Interact with an Image
9. Analysis of Pixel Values
10. Computer Vision