BC DATA SCIENCE
Saved by @Alex1Powell

✨✨ BIG NEWS: We are hiring!! ✨✨
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 😎💻 👉 https://t.co/KcSVMb89yx #RSEng
We value expertise across many domains - members of our group have backgrounds in psychology, mathematics, digital humanities, biology, astrophysics and many other areas 🧬📖🧪📈🗺️⚕️🪐
https://t.co/zjoQDGxKHq
/ @DavidBeavan @LivingwMachines
In our everyday job we turn cutting edge research into professionally usable software tools. Check out @evelgab's #LambdaDays 👩‍💻 presentation for some examples: https://t.co/GzO9m1CGz4
We create software packages to analyse data in a readable, reliable and reproducible fashion and contribute to the #opensource community, as @drsarahlgibson highlights in her contributions to @mybinderteam and @turingway: https://t.co/pRqXtFpYXq #ResearchSoftwareHour
To know more, here's a day in the life of a Research Data Scientist in our group, @louise_a_bowler ! 💯👩‍💻
https://t.co/1KPERqj7If
We are expert collaborators and work with the @turinginst community and beyond to enhance the applicability of their research. Check out the spotlights of some of our members 🙌:
https://t.co/KknkBtl6bg / @CamilaRangelS @radka_jersak @louise_a_bowler
We often host and work together with members of the civil service @faststreamuk program - see @Kevinzhangxu's thread about his experience with us earlier this year 👌: https://t.co/0UeLNhIJ9L
We highly value the development and career progression of our members: we have a dedicated Junior training role, and through our internal promotion scheme people have been promoted to Senior, Principal and even to Director! If you'd like to know more, contact @martinoreilly!
Some of our projects are covered in the Turing Podcast 🎙️, started by @EChalstrey, a (former Junior!) Research Data Scientist from the team: https://t.co/kmpPE1UldD
We work on topics ranging from data security 🔐 to air traffic control ✈️, from modelling contact-tracing during the current pandemic 😷 to the industrial revolution 🚂 (@LivingwMachines): to find out more, see our website! 👇
https://t.co/PaYng3c5Qa
Or check out our brand new @TuringDStories project!! 😻😻https://t.co/PEWnUMxoXc / @DavidBeavan @CamilaRangelS @Kevinzhangxu
And now, about you...
If you understand the importance of good practices for producing reliable software and reproducible analyses (as described for instance by @turingway), are fluent in a programming language used in #DataScience, and love to learn new skills, we strongly encourage you to apply!
In addition, if you feel that your profile would support the Group's activities, we would love to hear from you! Look at our historian turned into data scientist 👨‍💻: https://t.co/WJyc77kt49 / @f_nanni @LivingwMachines
Or at the contributions of our seismologist @kasra_hosseini to research in #digitalhumanities
https://t.co/0jPjUXvtOu
Experience in teaching and training, building #opensource communities, scientific computing and other themes are also very welcome - surprise us!
In particular, if you have worked (or would love to work!) on cloud ☁️ or high performance computing get in touch! Tomas is available if you'd like to know more 😉:
https://t.co/dgkK45JzN6 #HPC
We welcome any informal inquiries and will set up drop-in sessions if you'd like to have a chat with members of the group before applying. Just send a message to @f_nanni or @CamilaRangelS, or subscribe here:
📨 https://t.co/3e4b0dsEDJ
Again, consider applying or pass it on to colleagues and friends who might be interested!!
👉 https://t.co/LnGW7JNQX5

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Sunrit Jana \U0001f680
Sunrit Jana 🚀
@JanaSunrise
Pandas is an amazing data analysis and manipulation library for python, Really popular when working with ML, Data science or more.

It has a robust data structure, Dataframe for manipulation and analyzing data.

Here's some tips to help you work better with pandas. Let's go! ↓

If you're not aware about what a Dataframe is, It's an optimized data structure for loading data, analysing it, manipulating data in it, and Mostly gathering insights.

It uses Cython backend which transpiles into C for optimized code.

Here's how a dataframe looks like,


Before we start, You need to ensure, you have pandas installed. If you don't, Do that before moving ahead!

Here are the tips, Let's go!


1/ Convert PD series to Dataframe

We all have struggled, when we deal with pandas series. It's always easier to work with Dataframes, rather than series. Here is how you can convert series to dataframe easily.


2/ How to create dummy Dataframe for testing

We always need dataframes for testing and analysing normally, if we do not have data ready. Here is how you can use Pandas API to generate different types of data.
CODING RESOURCE , DATA SCIENCE
Ryan J. Gallagher
Ryan J. Gallagher
@ryanjgallag
Tired of word clouds? Want to do better sentiment analysis? Not sure how to look at the words underneath your measures?

Our long overdue paper on generalized word shift graphs is finally here!
https://t.co/lIBXvbMJWX
https://t.co/vSL1REYT8V

So what are they?

1/n


If we have two texts, there are many ways we can compare them. Weighted averages are a particularly useful measure because they're flexible and interpretable

Proportions, Shannon entropy, the KLD, the JSD, and dictionary methods can all be written as weighted averages

2/n

But weighted avgs are also slippery. When we try to compress complex phenomena like happiness, surprise, divergence, or diversity into a single number, it can be unclear what we're measuring

If the measure goes up, what does that mean? Why did it do that? Can we trust it?

3/n

Very often, that's the end of the line and we're left with an uneasy feeling in the pit of our stomach that our weighted avg is actually picking up a data artifact or some other unintended peculiarity

Word shift graphs help us address those concerns

4/n

First, word shifts look under the hood of weighted averages to see what's going on

All weighted averages are a sum of contributions from individual words. We can pull out those words, and rank which ones contribute the most to the difference between two texts

5/n
DATA SCIENCE
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Greg Yang
Greg Yang
@TheGregYang
1/ A ∞-wide NN of *any architecture* is a Gaussian process (GP) at init. The NN in fact evolves linearly in function space under SGD, so is a GP at *any time* during training. https://t.co/v1b6kndqCk With Tensor Programs, we can calculate this time-evolving GP w/o trainin any NN


2/ In this gif, narrow relu networks have high probability of initializing near the 0 function (because of relu) and getting stuck. This causes the function distribution to become multi-modal over time. However, for wide relu networks this is not an issue.

3/ This time-evolving GP depends on two kernels: the kernel describing the GP at init, and the kernel describing the linear evolution of this GP. The former is the NNGP kernel, and the latter is the Neural Tangent Kernel (NTK).

4/ Once we have these two kernels, we can derive the GP mean and covariance at any time t via straightforward linear algebra.


5/ So it remains to calculate the NNGP kernel and NT kernel for any given architecture. The first is described in https://t.co/cFWfNC5ALC and in this thread
DATA SCIENCE
Sophie Hill
Sophie Hill
@sophie_e_hill
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!
DATA SCIENCE
John Myles White
John Myles White
@johnmyleswhite
This post is pretty bizarre, but it manages to hit on so many false beliefs that I've seen hurt junior data scientists that it deserves some explicit

(1) The notion that R is well-suited to "building web applications" seems totally out of left field. I don't feel like most R loyalists think this is a good idea, but it's worth calling out that no normal company will be glad you wrote your entire web app in R.

(2) It is true that Python had some issues historically with the 2-to-3 transition, but it's not such a big deal these days. On the flip side, I have found interesting R code that doesn't run in modern R interpreters because of changes in core operations (e.g. assignment syntax).

(3) "Most of the time we only need a latest, working interpreter with the latest packages to run the code" -- this is where things get real and reveal some things that hurt data scientists. If this sentence is true, it's likely because you don't share code with coworkers.

(3) Really is a broader issue in data science: people only think of what they need to do their work if no one else existed and code was never maintained. Junior data scientists almost always operate on projects they start from scratch and don't have to maintain for long.
DATA SCIENCE

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Today is “Korean American Day”.

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102 Korean men, women, and children arrived in Honolulu aboard the SS Gaelic to work in Hawaii’s sugar plantations. By 1905, Koreans comprised 11% of Hawaii’s plantation workforce—around 7000 people.


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Stephen Fasenfeld \U0001f600
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