This is a more wonky thread about how I made this visualization in #Rstats using the awesome visNetwork

First step is to create the underlying network data. We need one file of "nodes" - i.e. the people and organizations. And one file of "edges" - i.e. the connections between them.

I created these by hand, based on excellent investigate journalism:
Now we can pull these together to create a network visualization!

You'll notice that I included a column for "type" in the nodes file. This allows me to use different icons for people vs firms vs political organizations.
All the icons are taken from @fontawesome. I *think* the visNetwork 📦 currently only works with fontawesome version 4.7, which is a bit limited – e.g. I decided to use a book icon to represent the fringe Evangelical Christian sect "Exclusive Brethren"! 😂
I very much enjoyed getting to use the "incognito" icon to represent all the unknown donors that have funded Tory MP Owen Paterson's overseas jaunts!
The icons are also scaled by how many "edges" connect to each "node".

Unsurprisingly, this means that the UK government and the Conservative party emerge as the most connected nodes in this network!
The great thing about visNetwork 📦 is that it's SO easy to make this visualization interactive with #RShiny.

You can add pop-up boxes ("tool-tips") that show more information when the user hovers over a node or edge – perfect for linking to the original reporting that I used.
Check out the full code and data on github! https://t.co/rWuCxbCnW3

More from Data science

I have always emphasized on the importance of mathematics in machine learning.

Here is a compilation of resources (books, videos & papers) to get you going.

(Note: It's not an exhaustive list but I have carefully curated it based on my experience and observations)

📘 Mathematics for Machine Learning

by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong

https://t.co/zSpp67kJSg

Note: this is probably the place you want to start. Start slowly and work on some examples. Pay close attention to the notation and get comfortable with it.


📘 Pattern Recognition and Machine Learning

by Christopher Bishop

Note: Prior to the book above, this is the book that I used to recommend to get familiar with math-related concepts used in machine learning. A very solid book in my view and it's heavily referenced in academia.


📘 The Elements of Statistical Learning

by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie

Mote: machine learning deals with data and in turn uncertainty which is what statistics teach. Get comfortable with topics like estimators, statistical significance,...


📘 Probability Theory: The Logic of Science

by E. T. Jaynes

Note: In machine learning, we are interested in building probabilistic models and thus you will come across concepts from probability theory like conditional probability and different probability distributions.
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Amazing Research Software Engineer / Research Data Scientist positions within the @turinghut23 group at the @turinginst, at Standard (permanent) and Junior levels 🤩

👇 Here below a thread on who we are and what we

We are a highly diverse and interdisciplinary group of around 30 research software engineers and data scientists 😎💻 👉
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In our everyday job we turn cutting edge research into professionally usable software tools. Check out @evelgab's #LambdaDays 👩‍💻 presentation for some examples:

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