2024-12-30 Techtonique's tutorial
# Techtonique tutorial
This tutorial (under the **Creative Commons license Attribution-NonCommercial-ShareAlike 4.0** International) is a step-by-step guide to using the Techtonique platform: https://www.techtonique.net.
The tool is designed to help you make informed, data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization.
The tutorial covers the basics of the platform, including how to upload data, how to use the different models, and how to interact with the [Application Programming Interface](https://www.techtonique.net/howtoapi) (API).
The contents are organized as follows:
- **Visual resources**: A short video showcasing the platform's features.
- **Audio resources**: A podcast describing the platform's features.
- **Slides**: A slide deck summarizing the platform's features.
- **Code**: Code examples demonstrating the platform's API features for various programming languages.
- **Excel**: A file containing VBA code to interact with the platform's API (Read the code to understand what it does)
- **R**: A file with R code to interact with the platform's API. In R console, run `source("./06-r-resources/r-example.R", encoding = "UTF-8")`
- **Python**: A file with Python code to interact with the platform's API. Run `python3 05-python-resources/python-example.py` to see the results.
- **JavaScript**: A file with JavaScript code to interact with the platform's API (interactive graph, read the code to understand what it does)
- **Command line**: A file with command line code (`curl`) to interact with the platform's API
Both clickable web interfaces and Application Programming Interfaces (APIs) are available in the platform. The cool point about APIs is that they are programming language-agnostic (supporting Python, R, JavaScript, etc.), relatively fast, and require no additional package installation before use. This means **you can keep using your preferred programming language or legacy code/tools**, as long as it can _talk_ to the internet. **In doubt? Ask your IT team.**
Currently, the **available functionalities** include:
- [Data visualization](https://en.wikipedia.org/wiki/Data_and_information_visualization): Which variables are correlated?
- [Probabilistic forecasting](https://en.wikipedia.org/wiki/Probabilistic_forecasting): Range of my projected sales?
- [Machine Learning](https://en.wikipedia.org/wiki/Machine_learning) (regression or classification) for tabular datasets: What is the price range of an apartment based on its size and number of rooms?
- [Survival analysis](https://en.wikipedia.org/wiki/Survival_analysis), analyzing *time-to-event* data: How long might a patient live after being diagnosed, and how accurate is this prediction?
- [Reserving](https://en.wikipedia.org/wiki/Chain-ladder_method) based on historical insurance claims data, how much is necessary to cover future potential accidents?
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A tutorial with slides, a short video, a podcast and code samples
Size
47.1 MB
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