HSMA - Modern Analytics
R
55
(Coming Soon) Why Learn and Use R?
Preface
Skills So Far
1
About This Section
2
Programming in Python
3
Discrete Event Simulation
4
Geographic Visualisation and Optimization Problems
5
Machine Learning
6
Natural Language Processing
7
(Coming Soon!) System Dynamics and Agent Based Simulation
8
(Coming Soon!) Git and Github
9
(WIP) Creating Web Apps and Dashboards in Python
Data Manipulation and Visualisation
10
(Coming Soon) Recap: Pandas
11
(Coming Soon) Recap: Graphs with Plotly Express
12
Example Outputs
An Introduction to Reproducible Document Creation with Quarto
13
What is Quarto?
14
Installing and Setting Up Quarto
15
Writing and Previewing Your First Quarto Document
16
Quarto Document Elements
17
Code Chunks
18
(Coming Soon) An Introduction to YAML Quarto Document Headers
19
(Coming Soon) Layout Customisation
20
(Coming Soon) Parameters
21
(Coming Soon) Automating Quarto Document Generation
Advanced Reproducible Documents with Quarto
22
(Coming Soon) Dashboard Layouts in Quarto
23
(Coming Soon) Slides
24
(Coming Soon) Scrollytelling
25
(Coming Soon) Creating eBooks and Printable Books in Quarto
Automating Data Gathering
26
(Coming Soon) Connecting to Databases
27
(Coming Soon) Getting Data from the Web with APIs
28
(Coming Soon) Web Scraping
Further Automation
29
(Coming Soon) Automating Spreadsheet Creation with the XlsxWriter Package
30
(Coming Soon) Automating Advanced Powerpoint Slides with the PythonPPTX Package
31
(Coming Soon) An Introduction to Automated Code Testing
32
(Coming Soon) Using Task Scheduler for Automation
33
(Coming Soon) An Introduction to Pipelines
Forecasting
34
(Coming Soon) An Introduction to Forecasting Methods
35
(Coming Soon) Time Series Concepts
36
(Coming Soon) Working with Time Series Data in Pandas
37
(Coming Soon) Naive Forecasts
38
(Coming Soon) ARIMA and SARIMA
39
(Coming Soon) Prophet
40
(Coming Soon) Other Time Series Models
41
(Coming Soon!) Ensembles in Time Series Modelling
42
(Coming Soon) Further Reading on Forecasting
Other Time Series Methods
43
(Coming Soon) Statistical Process Control
44
(Coming Soon) Changepoint Analysis
Alternative Forecasting Techniques
45
(Coming Soon) Monte Carlo Simulation
46
(Coming Soon) Machine Learning for Forecasting
Process Mining
47
(Coming Soon) An Introduction to Process Mining
Statistics
48
(Coming Soon) An Introduction to Statistics
49
(Coming Soon) Statistical Terminology
50
(Coming Soon) T-Tests
51
(Coming Soon) ANOVA
52
(Coming Soon) Chi Squared Tests
53
(Coming Soon) Time-to-event Data and Survival Analysis (Kaplan-Meier Curves)
54
(Coming Soon) Further Reading on Statistics
R
55
(Coming Soon) Why Learn and Use R?
56
(Coming Soon) Introduction to the Tidyverse
57
(Coming Soon) R Packages Without Python Equivalents
58
(Coming Soon) Using R and Python Together
59
(Coming Soon) R-Inspired Python Packages
Network Analysis
60
(Coming Soon) An Introduction to Network Analysis
Good Coding Habits
61
(Coming Soon) Documentation
62
(Coming Soon) Linting
63
(Coming Soon) Modularization
64
(Coming Soon) Code Review
65
(Coming Soon) Writing Automated Tests for Your Code
66
(Coming Soon) Creating and Publishing your Own Packages
Communicating Data
67
(Coming Soon) An Introduction to Good Communication of Data
R
55
(Coming Soon) Why Learn and Use R?
55
(Coming Soon) Why Learn and Use R?
54
(Coming Soon) Further Reading on Statistics
56
(Coming Soon) Introduction to the Tidyverse