readme

Everything you need to know about UCL DSS workshops!

About

Welcome to the UCL Data Science Society ✨ ! This is a quick starter pack for a Science executive and anyone else who is involved in the planning, design, scheduling, implementation and maintenance of the workshops.

Acknowledgements

Big shout out to @oleksiysmola Alex for implementing more than ten amazing workshops two years agao as the Head of Science and continuing to contribute to the work at Science last year. Without his legacy workshop materials, we would never be able to achieve what we have today. Thanks, Alex!

A further shout out to Tony for contiuning this great work as Head of Science last year, updating existing material and developing several new workshops alongside his team of executives! Again, without his work we would not be able to continue to build on the success of this society!

Workshops Available

This year, we have catagorised our workshops into four main themes:

• Introduction to Python Programming 💻
• Toolkits for Data Scientists 🔬
• Data Science with Python 🔮
• Data Science Fields 🥼

Legend:

• 🟥: Not implemented
• 🟨: Legacy material available, to be updated or re-written
• 🟦: Available, modification required
• 🟩: Good to go

Introduction to Python Programming 💻

Code	Topic	Prerequisite	Assigned To	Status
PY01	Fundamentals	None	Philip	🟩
PY02	Sequence: Lists and Tuples	PY01	Philip	🟩
PY03	Logic	None	Philip	🟩
PY04	Functions	PY01 PY02	TBC	🟥
PY05	Object Orientated Programming	PY01 PY02 PY03 PY04	Philip	🟩
PY1x	Algorithms	PY01 PY02 PY03 PY04	All	A series, TBC
PYTx	Troubleshooting Sessions: PATH, Jupyter Notebooks, Intepreters, pip and conda	None	All	A series, TBC

Toolkits for Data Scientists 🔬

Code	Topic	Prerequisite	Assigned To	Status
TK01	Numpy	Basic Python	Philip	🟨
TK02	Pandas	Basic Python	TBC	🟨
TK03	matplotlib	Basic Python	TBC	🟨
TK04	Git and GitHub	None	Philip	🟩
TK05	SQL	None	Philip	🟩

Data Science with Python 🔮

Code	Topic	Prerequisite	Assigned To	Status
DS01	Linear Model Regression	TK01 TK02 TK03	Zeyan	🟨
DS02	Logistic regression	TK01 TK02 TK03 DS02	Seda	🟨
DS03	Ridge and Lasso Regression	TK01 TK02 TK03 DS01 DS02	Philip	🟩
DS04	Decision Trees	``TK01 TK02` `TK03`	Philip	🟩
DS05	Random Forest	TK01 TK02 TK03 DS04	Philip	🟩
DS06	Support Vector Machine	TK01 TK02 TK03 DS04 DS05	TBC	🟨
DS07	K-means clustering	TK01 TK02 TK03	Philip	🟩
DS08	Hierarchical clustering	TK01 TK02 TK03 DS07	Philip	🟩
DS09	[DBScan clustering]	TK01 TK02 TK03 DS07 DS08	TBC	🟥
DS10	Dimensionality reduction	TK01 TK02 TK03	Philip	🟩
DS11	Introduction to Neural Network	Basic Python	TBC	🟨

Data Science Fields 🥼

Code	Topic	Prerequisite	Assigned To	Status
DSF01	Data Science for Finance	PY01, PY02, PY03	Zeyan	🟥
DSF02	Spatial Data Science	PY01, PY03, PY03, DS03, DS02	Philip	🟥

Legacy workshops 🛌

DS12| CNNs | DS11 | Sebastian, Stefania | 🟨 DS13| k-NN | DS01 DS02 DS03 | Tania | 🟨 DS14| Word Embedding | DS11 | Sebastian, Stefania | 🟨

Contributing to Workshops

In order to maintain the consistency and quality of our workshops, please follow these rules throughout your implementations.

Format

A recommended format for Python programming related workshops is Jupyter Notebooks. Other formats include Markdown, PDF, PowerPoint and so on. The following are some guidelines to follow.

Jupyter Notebooks

• Title: <Code> - <Theme>:<Topic>, e.g. PY01 - Introduction to Python Programming:Fundamentals

• Use clear sub-title structure throughout the notebooks (h1to h6 in Markdown syntax)

• Use Markdown syntax throughtout the notebooks: e.g. italic, bold, bold and italic, Object,inlineCode(), quoteblocks and codeblocks and so on. This document is an example.

• Keep a consistency. For instance, I prefer to use a quoteblock for definition:

  Computer Science: The study of computation and information. Computer science deals with theory of computation, algorithms, computational problems, and the design of computer systems hardware, software, and applications.

• Use LaTeX for mathematical formulae and expressions, both inline and equation blocks.

• Use relative path instead of absolute path for inserting pictures, if there is any. Put pictures in assets/

• Remove the answers from the questions and exercises, or put them in to different codeblocks to reserve spaces for attempting

Markdown

All of above, and:

• If possible, export a PDF version using Typora with theme Ursine Umbrella

PowerPoint

• Use DSS logo on every page.

To be continued

Structure

Alex's legacy workshops had set a plausible structure for workshops. Example would be:

.
├── some-workshop
│   ├── README.md
│   ├── workshop.ipynb
│   ├── problem.ipynb
│   ├── answer.ipynb
    └── assets
        └── figure1.png

• workshop: the workshop material in .ipynb, .pdf, .md, .ppt and so on
• problem: exercises with no answers on it
• answer: answers to problem
• README: a syllabus of the course
• assets/: where you put the pictures and attachments to

README

The README should have:

• Title
• Description
• Prerequisite (use workshop code if possible)
• Author and How to Contact Author
• Objectives and Outcome
• Outline (just copy and paste the title structure from h1 to h6 in your workshop in a tree structure)

Project Management at Science

The kanbans are used for project management:

• Data Science with Python Kanban
• Toolkits for Data Scientists Kanban
• Introduction to Python Programming Kanban

Create an issue in each repository to represent a TODO. The issues will be added to the Kanban as a card with a person assigned to it. Label the cards well. Close the issue when finished.

Workflow

1. Create a repository with UCL-DSS account, the name of thye repository should be <keyword>-workshop (e.g. python-logic-workshop)

2. Fork the repository to your own warehouse. Maintain your own repository.

   Keep commits small and use multiple commits. E.g. Commit#1: Write introduction Commit#2: Update question 1 instead of Commit#1: Implement workshop 1

3. Create issue for TODOs. Keep comments and communications on the issues to that issue thread. Keep track of the kanbans to ensure your tasks are managed.

4. Make a pull request to the main branch of the UCL-DSS repository when finished. Link that pull request to the issues related. I will merge it and close the issues manually.

Scheduling of Workshops

Since this academic year the lectures are mostly pre-recorded, so Monday to Wednesday should be a good fit to students' schedule. Each week, I will issue a ticket to the relavent executives and FYR with a workshop to do. The executive will contact Marketing and provide your marketing information. Usually, that includes:

• Your name
• Your picture
• Theme
• Code
• Title
• Prerequisite
• Difficulty (1~5)
• GitHub repository link
• Preparation needed
• Time and Date using London Time - you decide on which day (Mon ~ Wed) and exact time to host the workshop. With consideration for people in UTC+1/+0 to UTC+8

All workshops are hosted using our MS Teams channel and will be recorded. Most of the information can be found above.

Collaboration with Imperial College

Hello to our fellow young data scientists from South Kensington! Greeting from Gower Street!

To be continued