BC LEARNING
Saved by @vasantsri_

The MOST overlooked skill in data science...

And it could get you a $20,000 promotion.

Here's how. 🧵

#datascience #stats #rstats

 Text analysis is a treasure trove of predictive information.

Yet 90% of companies don’t know how to use it.

They are overlooking one of the most powerful and predictive sources of information…
In fact, text analysis is part of the strategy that I used to help double my business in 2022...

While companies like Facebook (Meta) lost 70% of their value.

My company went from $0.9M/year to $2,000,000 per year.

Using text analysis was a key piece.
So imagine how valuable you could become to your company if you doubled the part of the business you touch...

In less than 12 months.

They'd be more than happy to give you a $20,000 raise...

You just made them $1M. (or more)
But you need to learn Text Analysis so you can improve model performance...

Which leads to:

1. a better understanding of customers

2. a better understanding of products

3. better lead scoring

4. better marketing

5. $1,000,000+ ROI
Learning text analysis is full of pitfalls...

Yeah, there's a ton of text data available.

But which do you use?
The mistake every data scientist makes is trying to use every source of data...

And they always go for unstructured text first.

Big mistake.
The structured data is actually EASIER and MORE VALUABLE.

So start there.
Now you're probably thinking, great idea! But how?

How 'bout I show you...
I'd like to help you learn Text Analysis for free.

I put together a FREE workshop where I'll uncover how I do text analysis.

Here's what you learn:
1. How to work with text data

Learn how to tokenize structured and unstructured text so you can make it useful for solving business problems
2. My simple technique for automating text data mining

It's 1/10th of the code versus how the way I'm seeing others do it
3. How to improve your model performance with text data

Get the insider's secret to model performance increase + make your company more profitable
4. Get ideas for your future job portfolio

Expand your data science tool chest with Text Analysis
What's Your Next Step?

My Text Analysis Workshop is completely free.

All you need to do is register & show up.

https://t.co/cA1nVZhVY3

More from Learning

Rob - Nothing Barred Fitness
Rob - Nothing Barred F...
@Rob_NBF
How to spend 80% less time in the gym and get BETTER results for building muscle than you're getting now (SERIOUSLY)

Backed up by science.

Thread 👇


First mistake:

Doing too many exercises.

All you need is:

- A squat or similar movement
- A hip hinge
- Vertical push
- Vertical pull
- Horizontal push
- Horizontal pull

Add in some calf work and ab work.

You don't need multiple exercises for each muscle group.

Some people do 3 or 4 different exercises for each of the above, e.g. a flat bench press, a decline bench press, a pushup, an incline dumbbell press... this is a waste of time.

All of those are using the same muscles and fall into the "horizontal pull" category.

All you need to do is pick ONE for each category and lift to failure with good form.

Reaching failure (or close) creates the stimulus for adaptation.

Adding more work does not increase this stimulus, it just adds more muscle damage that you've got to recover from.

The other mistake is that people do too many sets.

You've all seen the bros in the gym on chest day...

HAMMERING the chest with 5 sets of flat bench press, 5 sets of incline, 3 sets of decline, 5 sets of flyes...

This is just cutting into recovery and making results worse.
LEARNING
Ross \U0001f4a1
Ross 💡
@THEROSSHARKNESS
11 books that will change your life:

1. Think and Grow Rich by Napoleon Hill

Lessons:

- Your thoughts are more powerful than you think
- Your why is powerful


2. Can’t Hurt Me by David Goggins

Lessons:

- You are capable of much more than you think
- Pain is inevitable, suffering is optional


3. The 4 Hour Work Week by Tim Ferris

Lessons:

- It’s possible to work less but earn more
- The goal isn’t necessarily more money


4. Awaken The Giant Within by Tony Robbins

Lessons:

- How you talk to yourself is everything
- Change happens in an instant
LEARNING
Advertisement
Suman | Data Scientist
Suman | Data Scientist
@datawithsuman
Top Data Science courses from Google 🚀

A thread 🧵👇🏻

1/ Big Data & Machine Learning

2/ Perform Foundational Data, ML, and AI Tasks in Google

3/ Create ML Models with BigQuery

4/ Insights from Data with
LEARNING
MATT GRAY
MATT GRAY
@matt_gray_
If you want to give yourself a personal MBA, read these 10 books:

Zero to One
by Peter Thiel

Subject: Strategy and Positioning

Lessons:

• Last can be first
• Rivalry causes us to copy the past
• Progress comes from monopoly, not competition


Expert Secrets
by Russel Brunson

Subject: Marketing

Lessons:

• Maintain absolute certainty
• The riches are in the niches
• Convince with emotion first, then logic


$100M Offers
by Alex Hormozi

Subject: Sales

Lessons:

• Create grand slam offers
• Find the starving crowd
• The fundamental equation of value


Deep Work
by Cal Newport

Subject: Productivity

Lessons:

• Embrace boredom
• Create daily rituals
• Find your work-style
• Treat work like a craft
LEARNING
Savio Martin \u26a1\ufe0f
Savio Martin ⚡️...
@saviomartin7
Best YouTube Channels to learn Web Development

A Thread 🧵👇️

1. FreeCodeCamp

FreeCodeCamp is a non-profit organisation that'll teach to learn to code for free.

https://t.co/TfMCxjaE0r 🔗

{ 1 / 10 }


2. Traversy Media

Traversy Media creates crash courses to learn programming in a very easy way. He also collaborates with great creators.

https://t.co/EwwtdO0vRw 🔗

{ 2 / 10 }


3. codeSTACKr

codeSTACKr creates useful content on web development including HTML, CSS and JavaScript.

https://t.co/4Pww7BcMRd 🔗

{ 3 / 10 }


4. Code with Ania Kubów

Ania Kubów is a software developer who creates videos on building amazing apps from scratch.

https://t.co/6Hwrfiirw3 🔗

{ 4 / 10 }
LEARNING

You May Also Like

The '60s at 60
The '60s at 60
@the_60s_at_60
"The MAD Primer of Bigots, Extremists and Other Loose Ends," from September 1969.

Seen a couple of these panels make the rounds from time to time, so here's the complete set of 10 (something to amuse and/or offend almost everyone).


Chapter 1: The Super Patriot


Chapter 2: The Ku Klux Klansman


Chapter 3: The American Student


Chapter 4: The Right-Wing Extremist
FUN
Tinker \u274e
Tinker ❎
@TinkerSec
Singing the Blues:
Taking Down an Insider Threat

"I had all of the advantages. I was already inside the network. No one suspected me. But they found my hack, kicked me off the network...

...and physically hunted me down."


Many pentests start from the outside, wanting to see how the perimeter might be breached.

This pentest started from the inside. My client wanted to assume they had already been breached, and, if breached, how far could an attacker go.

Could they stop me once I was inside?

So they snuck me in. Disguised me as a new employee. Gave me a work computer, an ID badge, an account in their system... hell, I even had a cubicle w/my assumed name on it.

The only person who knew who I really was was their CISO. Everyone else thought I was Jeremy in Marketing.

During most of the first morning, I completed onboarding, made introductions, and completed menial tasks.

But I had to act quick. I only had a week onsite. I had to hack their network while not raising suspicion.

So I set about it.

You have to understand... most "Internal Pentests" are straight forward. The hard part is breaching the network, but once you're inside, it's a target rich environment. End of Life computers, default passwords, everyone a Local Administrator...
TECH
Advertisement
Roger Grosse
Roger Grosse
@RogerGrosse
Important paper from Google on large batch optimization. They do impressively careful experiments measuring # iterations needed to achieve target validation error at various batch sizes. The main "surprise" is the lack of surprises. [thread]

https://t.co/7QIx5CFdfJ


The paper is a good example of lots of elements of good experimental design. They validate their metric by showing lots of variants give consistent results. They tune hyperparamters separately for each condition, check that optimum isn't at the endpoints, and measure sensitivity.

They have separate experiments where the hold fixed # iterations and # epochs, which (as they explain) measure very different things. They avoid confounds, such as batch norm's artificial dependence between batch size and regularization strength.

When the experiments are done carefully enough, the results are remarkably consistent between different datasets and architectures. Qualitatively, MNIST behaves just like ImageNet.

Importantly, they don't find any evidence for a "sharp/flat optima" effect whereby better optimization leads to worse final results. They have a good discussion of experimental artifacts/confounds in past papers where such effects were reported.
MACHINE LEARNING
Filippo Valsorda
Filippo Valsorda
@FiloSottile
“We don’t negotiate salaries” is a negotiation tactic.

Always. No, your company is not an exception.

A tactic I don’t appreciate at all because of how unfairly it penalizes low-leverage, junior employees, and those loyal enough not to question it, but that’s negotiation for you after all. Weaponized information asymmetry.

Listen to Aditya


And by the way, you should never be worried that an offer would be withdrawn if you politely negotiate.

I have seen this happen *extremely* rarely, mostly to women, and anyway is a giant red flag. It suggests you probably didn’t want to work there.

You wish there was no negotiating so it would all be more fair? I feel you, but it’s not happening.

Instead, negotiate hard, use your privilege, and then go and share numbers with your underrepresented and underpaid colleagues. […]
LIFE
Emma Hilton
Emma Hilton
@FondOfBeetles
This is Kuinini ‘Nini’ Manumua, the woman who was ultimately displaced by inclusion of Laurel Hubbard.

She’s 21, and it would have been her first Olympics.
ALL