Discover and read the best of Twitter Threads about #datascience

Most recents (24)

Is there more questions on a voting ballot than there are questions on the SAT? How the can they do it in a somewhat timely manner but our government can't grasp the concept of how to do it...still, today?


Stop asking stupid questions or you'll continue to get stupid answers.

Feed us the data that matters so we can figure this out the right way.

#ProblemSolving101 using the #ScientificMethod.
If facts tend to change on their way to finding the truth...then give us the facts and lets figure this out for ourselves once and for all.

Stop censoring the truths and facts that are available.
Read 20 tweets
Daily Bookmarks to GAVNet 07/29/2020…
Futures Fallacies - Our Common Delusions When Thinking About the Future * Journal of Futures Studies…

#fallacy #future #thinking #futures
Full article: Re-assembling the surveillable refugee body in the era of data-craving…

#Data #DataScience #DataMining
Read 6 tweets
I spent the weekend putting together a "Meta RMarkdown" blog post!

4 R Markdown Strategies:

1 - Literate Programming
2 - Data Product
3 - Control Document
4 - Template…

#rstats #datascience #tidyverse
1 - Literate Programming

Use RMarkdown like a reproducible scientific notebook, capturing code, comments, and specific outputs in a output document.

All in plain text that is easily human-readable in version control!
2 - Data Product

Generate all sorts of fancy outputs from RMarkdown, such as:
- Presentations (Powerpoint or web native like remark.js)
- Dashboards w/ flexdashboards
- Reports as HTML, PDF, Word, etc
- Entire websites w/ blogdown, hugodown, distill
Read 6 tweets
I meet a lot of people interested in getting into #data. It’s really aspiring to see so many people wanting to understand more about data.

Here’s an updated #thread of FREE resources to help you upskill into a data analyst, scientist or engineer.

Remember data is everywhere!
#Facebook launched "Summer of Support" program that offers FREE training in digital marketing and data/insights.

6-week program of courses that offers free #Digital #Marketing participants advice, insights, information & support, especially for SMEs.
Analyttica is offering a FREE training on Data Analytics Fundamentals & another on Machine Learning.

Each training has virtual hands-on labs w/certificates for completing.…

#BlackTechTwitter #WomenWhoCode #100DaysOfCode #data #MachineLearning #analytics
Read 10 tweets
I am really thrilled to release #AutoSpill onto @biorxivpreprint. It is a novel method for applying compensation to #flowcytometry data, which reduces the error by ~100,000-fold. It is thanks to AutoSpill that we can push machines to their max colours…
#AutoSpill is a beautiful example of what #maths can add to #immunology, led by the talented Dr Carlos Roca.

It has really open up my eyes to the potential of #computationalbiology, and is the reason why we have a new #datascience position available:…
So how does #AutoSpill work? If you just want to compensate your data, simply upload your single colour controls to and then copy the spillover matrix to your #flowcytometry program of choice

@CarlyEWhyte can walk you through the whole process in <2'
Read 20 tweets
In January, I wrote a Python script to query the @USAJOBS API every day for federal job announcements containing any of eight keywords related to #datascience. Almost 6 months later, I have amassed more than 500 job announcements and want to explore what I've collected. A thread:
First, these are the eight keywords I searched for and the number of announcements in which they appeared in the title, qualifications, or duties:
The problem with looking for #datascience jobs on USAJobs is that there (currently) is no data science job series so you have to wade through other occupational series. These are the 10 most common job series that contained any of the keywords.
Read 11 tweets
If you are (, or know someone who is) planning to learn #DataScience / #MachineLearning / #AI by yourself, especially during this lock-down period, please know that it is totally possible... you only have 3 Jobs:


Short Answer:

1. Understanding the concept and usefulness of learning AI/ML;

2. Learning about the tools and best practices;

3. Getting your hands dirty with constant practice and real-world problems.


(Keep reading for the detailed answer, tips & links!)
Long Answer:

1. Understand ML from its First Principle - Intro & concepts

2. Learn some basic or more Python
- Take as much courses as you can here:…

Read 8 tweets
A few months ago I wrote a very long blog for @CommitteeSysmus on advice that I wish I would have heard when I was a graduate student about getting a non-academic job.…

I'll thread the major points below because it's a long one (4K words)
There are a lot of resources on how to keep going on in academia, but as we all know, we can't all immediately go from PhD to more academia (AND this was all originally written pre-COVID) but there's not a lot of good resources to help people prepare for that as a student.
So I wanted to just pass on many words of wisdom that I think I would have found helpful a few years ago (esp since my first job out of PhD was helping people land junior #datascience jobs) and not just say what to do (ewww) but rather my thoughts on why.
Read 14 tweets
Today's thread - by @pingali - is about making #MachineLearning projects successful. Links back to the first session of making #DataScience work by @scribbledata… (1/8) Learn more by joining The Fifth Elephant on
@pingali @scribbledata Framing: Formulating a good problem is the most important step. What is 'good' depends on the context. Mature companies require optimal solutions. Sometimes, framing can be good enough but something that can be pulled back. Take #risks, with guard rails in place. (2/8) Join The Fifth Elephant to participate:
@pingali @scribbledata Buy-in: #DataScience has ambiguity. Communicating is
critical - define goals of communication; who you are communicating with is important. Link data science outcomes to business outcomes. Understand incentives and motives of other stakeholders. Be empathetic. (3/8) Subscribe to The Fifth Elephant on to join.
Read 8 tweets
As #bioinformatician #biostatistician #DataScientist, have you ever felt you've done lots of work, yet achieved nothing? here is how to change that! A thread 👇 1/n
1 - #document rigorously your computing work - same as bench scientists do on experiments. Document your initial thoughts on data, your decisions, tests, issues, how you solved them, how you tried various tools, how you picked one over others. These are all *achievements*. n/1
2 - #track your tasks and efforts - make it quantitative. Write a weekly checklist on GitHub wiki; update and check it off at the end of a day. Version control your codes; push meaningful commits. Be aware of your time spent on each task. Numbers may be boring, but effective. 2/n
Read 13 tweets
I had so much fun working on this data science course!

One aspect of the fun I had was learning interesting information about the data I used. I share my learnings here and look forward to hearing about yours.

#julialang #datascience
The next time you visit Yellowstone National Park to check out the Old Faithful geyser, know that if you wait for too long for the geyser to go off... you are likely to witness a longer eruption.
We use a cars dataset of car models with features such as horsepower and cylinders (& 5 more). We perform dimensionality reduction on this data & find out that European/Japanese cars cluster together whereas American cars form their own two clusters. But why? I'd love to find out
Read 13 tweets
Had fun live-streaming a chat yesterday with @IcahnMountSinai Director of Bioinformatics @AviMaayan

Before our @tidybiology code along, Avi dropped some words of wisdom 👇

On approaches to science:

Avi Mayan builds tools to enable biological discovery. He wants to figure out “how cells work”

He is gene-agnostic & disease-agnostic, meaning he can follow science wherever it takes him

This is a tremendous advantage for genuine & impactful discovery
On Data science:

#datascience infuses computation in biology, statistics, topology (networks), dynamic modeling

All providing a data driven approach

For his PhD, Avi tried reading 1000+ papers to understand cell signaling networks

“It was quite daunting, and it didn’t work"
Read 7 tweets
Data is the new buzz word and it is made its way into the Ag sector,helping farmers make smart decisions in order to increase productivity.
Here are some of the benefits of data science

Data Science grants the farmer access to databases collected from examining the weather in
order to come to conclusions that would improve productivity.

Also in gathering data from the soil like uptake rate, land type etc, recommendations can be made as regards the optimal need of the soil for certain nutrients.

In modern agriculture, advanced algorithms are used to
identify the patterns and behavior of nature that helps in forecasting the invasion of pests and the spread of microscopic diseases.
Read 4 tweets
Es un orgullo para la Comunidad de Desarrolladores de Argentina poder acompãnar iniciativas como el #ConnectDay junto a estas empresas @plataforma5la, @distillerylatam, @revistasg y @clarikagroup 💪
¡Hoy es el #ConnectDay! Desde CoDeAr estamos felices de poder acompañar a @wtmriodelaplata, @GDGCordobaARG, @gdgriodelaplata en este día de charlas y de compartir conocimiento en comunidad. Podés sumarte a la transmisión en vivo desde acá:
Comienza la primer charla sobre #DataScience y #Economía, en el contexto de las #transdisciplinas.
Read 118 tweets
@AcademieAgri @Agreenium Philippe Stoop, membre de l'@AcademieAgri et directeur Recherche et Innovation @ITK_web Introduit les controverses sur les produits utilisés dans l'agriculture (pesticides, fongicides, aliments ultra-transformés), qui alimentent le discrédit des agences sanitaires. #NoFakeScience
@AcademieAgri @Agreenium Étudions : Pourquoi il est normal et légitime qu'il y ait des avis différents parmi les scientifiques sur les produits utilisés dans l' #agriculture, notamment en fonction des #données : sources, les notions de risque VS danger.
#noFakeScience #Recherche #RisquesSanitaires
@AcademieAgri @Agreenium Évaluation des risques : #toxicologie
Le travail de suivi des effets indésirables rentre ds les programmes de la Recherche Publique. Les résultats sont publiés dans les publications scientifiques.
Les industriels pour leur part, évaluent par le prisme du principe de précaution.
Read 11 tweets
How to move towards Quantitative Trading :

1) Learn Python
2) Learn key trading libraries & Pandas
3) Data visualisation using seaborn & matplotlib
3) Statistics for Financial Markets
4) Backtest your strategies
5)Optimise & Automate
6) ML insights for trading

#quant #trading
1) This is a good basic course on Udemy to learn about Python required for trading…

2) Learn about Pandas( cruciwl for data cruncing & handling time series data)…

Also install talib: an essential library for a trader. It has inbuilt functions for all technical indicators making our life easy.…

Read 9 tweets
Hilos de Hilos Temáticos
⚾ ✈️ 📷 🎧 ⚕️ ♻️ 🔥
🇹🇼 #CPBL
Read 20 tweets
What's the difference between a Data Engineer , a Data Analyst & a Data Scientist?

If you want to make sure you don’t lose your job in the next five years, you probably want to know something about Big Data, or even switch to a data-related career.
#DataScience #Data
You don’t need to be an excellent statistician or a high-class mathematician to work in data science or analytics, nor do you need a lot of prior programming knowledge.
- Dr. Rebecca Pope (Head, Data Science d Engineering at KPMG)

#DataScience #Data #DataAnalyst #Statistics
However, you do need an interest in statistics, you do need to be willing to learn how to code, and you do need to know how to do some high level mathematical operations. Data scientists are not just statisticians.

#DataScience #Data #DataAnalyst #Statistics
Read 12 tweets
0/ Essential philosophy for #DataScience, a thread of 32 questions.

Grab a friend (virtually) and tackle these 32 essential questions (all with more than one reasonable answer) that every serious #data professional should answer for themselves.

#philosophy #rstats #statistics
1/ Is it possible to know anything at all?
2/ What does it mean to "know" something?
Read 34 tweets
#DataProductManagement still follows the software development practices from #productmanagement standpoint. This essentially may not for #dataProducts. 1/n
2/n: Usually for building software products #productmanagement looks to understand #marketproblem = Many customers * Same/Similar Problem statement. This is them considered as an opportunity in #Enterprise #ProductManagement and gets the funding 2 build
3/n: #DataProducts on the contrary are build to solve unique problems within the company. @netflix building recommendation system to push content and then using the same data to understand which content to invest in.
Read 6 tweets
#Corona benefits.

A thread (will be continually updated).
@pluralsight #FreeApril offer::
Build in-demand tech skills without leaving your house. Get free access to 7,000+ expert-led video courses and more all month long.…
@Codecademy #students offer::
Codecademy Pro for free to high school and college students across the world for the rest of the school year.…
Read 20 tweets
Specific "new evidence"... #Masks4All #MasksAreNormal #MaskGate, I've linked each separately by date for all my and the #medtwitter #DataScience junkie friends below:

#Data #Science #COVID19

👉February 19, 2020

#Data #Science #COVID19


"Asymptomatic cases in a family cluster with SARS-CoV-2 infection"…
👉February 21, 2020

#Data #Science #COVID19


"Presumed Asymptomatic Carrier Transmission of COVID-19"…
Read 13 tweets
Blogs are great tools to showcase your work as a machine learning engineer. By writing blog posts, you better describe your projects and results. Also, it makes your projects discoverable by search engines.

#MachineLearning #DataScience
A practical way to start blogging is to use tools like @Medium or @ThePracticalDev. You don't have to waste time with details like domains, templates, or hosting. You have to focus only in writing.

#MachineLearning #DataScience
Read 3 tweets
If you are diving into Machine Learning some time after school, then you need a brush on Calculus. Probably you won't have the time to take a full course on Calculus, so you have to focus on the essential topics for MachineLearning. Check them:
1) Functions: you need to be familiar with functions, you must explain what a function is, and you need to be familiar with some of the main types of functions, like linear, quadratic, exponential, logarithmic and trigonometric functions
2) Derivatives: learn what is a derivative and how to find the derivatives of simple functions. Don't spend too much time on finding derivatives of complicated functions. Learn the basics and learn how to use numerical software to find derivatives.
Read 8 tweets

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