10 PYTHON 🐍 libraries for machine learning.

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Lately there have been various rumours in the media about myself and Jumbo in relation to Williams. To clarify things, Jumbo is not and will not invest any money in my career. 1/2

Of course we’re collectively looking at career opportunities, however this is certainly not our philosophy and strategy. I hope to announce my future plans soon, be patient 😉 2/2

So, you’re a writer and you’re worried about writing outside your immediate experiences and identities.

Here is the thread of practical advice for you.

EXAMINE YOUR MOTIVES. Why do you want to write this?

Are you trying to solve racism/sexism/colonialism with your work?

As writers we love stories about heroic writers whose work has changed the world. And as such we like to look to our own writing to solve societal problems.

And I understand this completely, not the least because I’ve felt the pull.

But if you’re looking to play saviour with your words, it is unlikely that you will do the marginalised people you are trying to save justice.

And I understand this very often comes from place of good intentions, but there is a reason that most of the moralising plays written by white abolitionists are deeply uncomfortable to read.

It is incredibly easy for works looking to play saviour to become patronising or traffic in simplistic stereotypes that ultimately hurt the people they are looking to rescue.

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.

Ever wonder how traders get in stocks before they explode? 🧐

Or find options that move 1,000% in a single day/week?🤯

I made a thread going over exactly how I find these stocks BEFORE they take off 🚀
👇👇👇

So, let's start with the software I use…

Finviz!! Finviz is a free website that I use to scan for stocks.

They have a lot of other cool features as well, but I only use their scanner.
👇

Head on over to finviz and go to the screener

Click on the “All” tab on the top and put in the criteria in the next tweet
👇

This is the first scan that I use for bullish swing setups
✅Quarter +10%
✅Week Down
✅Vol over 400k
✅Optionable
✅Price > $20
👇

The next scan I use is the opposite of the first one.

Simply change:
✅Quarter +10% to -10%
✅Week Down to Week up

This is my bearish scan for swing setups. 👇