Recap and Looking Beyond IDS

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.title[
# Recap and Looking Beyond IDS
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.subtitle[
## <br><br> Introduction to Data Science
]
.author[
### University of Edinburgh
]
.date[
### <br> 2024/2025
]

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University of Edinburgh
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## Reminders

- Check your html file knits and loads properly

- HDMI port on laptop, if you need converter let us know

- Don't forget your peer evaluation form

- Don't forget to add the course GitHub page to your project

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## What have you learnt?

| Week | Topic                                     |
|------|-------------------------------------------|
| 1    | Welcome and Toolkit                       |
| 2    | Importing and recoding data               |
| 3    | Wrangling and tidying data                |
| 4    | Visualising data                          |
| 5    | Communicating effectively                 |
| 6    | Ethics                                    |
| 7    | Programming and Functions                 |
| 8    | Predictive Modelling                      |
| 9    | Classification and model building         |
| 10   | Validation and uncertainty quantification |
| 11   | Looking beyond IDS                        |

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## Looking Beyond IDS

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## Books - I

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## Books - II

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## Courses

- [Statistics (year 2)](http://www.drps.ed.ac.uk/23-24/dpt/cxmath08051.htm)
  * Estimators, including MLEs
  * Hypothesis Testing 
  * Linear Regression with theory
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- [Statistical methodology (year 3)](http://www.drps.ed.ac.uk/23-24/dpt/cxmath10095.htm)
  * maximum likelihood estimation
  * likelihood ratio tests
  * Bayes theorem and posterior distribution
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- [Statistical computing (year 3)](http://www.drps.ed.ac.uk/23-24/dpt/cxmath10093.htm)
  * optimising functions in R
  * programming statistical techniques and interpreting the results (including bootstrap algorithms)

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## Courses

- [Machine Learning in Python (year 4)](http://www.drps.ed.ac.uk/23-24/dpt/cxmath11205.htm)
  * Supervised and Unsupervised learning methods
  * Coding with Python, two different projects.
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- [Statistical Case Studies (year 4)](http://www.drps.ed.ac.uk/23-24/dpt/cxmath10102.htm)
  * Two real-world statistical projects that are meant to simulate consultancy-style work in a business/research environment
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- [Mathematics Project (year 4)](http://www.drps.ed.ac.uk/24-25/dpt/cxmath10063.htm)

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## R capabilities: Special case

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## Shiny Apps

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- Shiny is an R package that makes it easy to build interactive web apps straight from R
- You can host standalone apps on a webpage or embed them in R Markdown documents or build dashboards
- You can also extend your Shiny apps with CSS themes, htmlwidgets, and JavaScript actions
- Learn more at [shiny.rstudio.com](https://shiny.rstudio.com/)
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<img src="img/shiny.png" width="60%" style="display: block; margin: auto auto auto 0;" />
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## High level view

- Every Shiny app has a webpage that the user visits, and behind this webpage there is a computer that serves this webpage by running R

- When running your app locally, the computer serving your app is your computer

- When your app is deployed, the computer serving your app is a web server

- If you want to try making your own, try working through the reading [here](https://mastering-shiny.org/basic-app.html)

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## Some others

- Dynamic reporting with Rmd files with different programming language

- Widely considered the best tool for making beautiful graphs and visualizations.

- Possible to find advanced statistics packages for high-level modeling.

**Key message**: If you learn a programming language to some extent, you can learn other tools intuitively  even if there are some differences on syntax. The battle of R vs Python is already over!

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## Research in Data Science

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## Turing Institute

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## Beyond your courses

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## Royal Statistical Society

They offer free membership to all students in full-time education with an interest in statistics and data science.

The membership is called e-student so find out [benefits from here](https://rss.org.uk/membership/join-now/e-student/)

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## Digital Skills Programme (UoE)

They offer various different training courses to develop your digital skills

- Introduction to Data Science

- Basic Data Visualizations

- Data and AI Ethics

- Introduction to R or Python

You can find more details from [digital skills programme for students](https://information-services.ed.ac.uk/help-consultancy/is-skills/classroom-based-courses-and-webinars)

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## Good luck!

for your project presentations and career after IDS...

Please take some time to complete the end-of-semester feedback survey.

You can access it [here](https://forms.office.com/e/38hF4Da3Bs)