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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elvis
@omarsar0
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.
DATA SCIENCE
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.
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Goku Mohandas
Goku Mohandas
@GokuMohandas
🔥 Putting ML in Production! We're going to publicly develop @madewithml's first ML service. Here is the broad curriculum:

- 📦 Product
- 🔢 Data
- 🤖 Modeling
- 📝 Scripting
- 🛠 API
- 🚀 Production

More details (lessons, task, etc.) here: https://t.co/xmMm9XGK9j

Thread 👇


Questions that this thread will answer:

- What is it?
- Who is this course for?
- What is the format?
- What makes this course unique?
- Why constrain to open source tools?
- What are my qualifications?
- Why is this free?
- What are the

What is it?

Putting ML in Production: a guide and code-driven case study on MLOps. We will be developing and deploying Made With ML's first ML service, from Product → ML → Production, with open source tools.

This ML service will act as a foundation for all future ML features and subsequent iterations. The first feature is tagifai - multilabel classification of tags for a project. We'll discuss the need and utility of this feature in the first lesson.

Who is this course for?

- ML developers looking to become end-to-end ML developers.
- Software engineers looking to learn how to responsibly deploy and monitor ML systems.
- Product managers who want to have a comprehensive understanding of the different stages of ML dev.
DATA SCIENCE
Emil Wallner
Emil Wallner
@EmilWallner
Tips for AI writers:

1. Spend 30% of your effort on skimming all student ML papers (e.g. Stanford NLP CS224n) the past 3 years and prototype your favorites

The idea is everything. Pick an area you are interested in and ideally something that has a visual aspect to it

Most of my 'on the top of my mind' ideas were bad in retrospect. Skimming 100s of student papers will give you an overview of what's interesting.

Student papers are overlooked, easy to understand, and have good compute constraints.

2. Spend 30% on your effort on coding

Create an edge to the project. Apply it to something new and use FastAI or Keras to improve the accuracy with 5-30%.

3. Spend 30% writing an in-depth article

Have a north star article in terms of structure and quality. Find something that stretches you to your utmost capability. I used @copingbear’s Style transfer article:

4. Spend 10% marketing your project

Invest a week in studying the strategies to rank on sites like HN and Reddit, then use them. If you have an interesting result and a great article, you've done the hard work.
DATA SCIENCE
Ryan J. Gallagher
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@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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I just finished Eric Adler's The Battle of the Classics, and wanted to say something about Joel Christiansen's review linked below. I am not sure what motivates the review (I speculate a bit below), but it gives a very misleading impression of the book. 1/x


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It's worth remembering that literacy itself is elite throughout most of this history. Literacy is, then, also the education of slaveowners. We can honor oral and musical traditions without denying that literacy is, generally, good. 5/x
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Here is a special thread introducing our series on “The Making of the Islamic World.” It offers 10 episodes about the transformation of Islamicate societies between the 7th and 17th centuries. You can find the series page here: https://t.co/7mL9eWmMmz


This is Chris Gratien posting. I created this series during Fall 2020 for my students as asynchronous material supported by readings and discussion. This is going to be a long thread, but it will be a great shortcut for those looking to make similar use of these materials.

There are two items worth pointing out: 1. This is an overview of the political, social, and cultural history of Islamicate societies. It does not deal with the origins of Islam or many theological questions. It does discuss Islam’s spread and the transformation of Islamic law.

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