Many of the biggest tech trends in data analysis can be seen as increasingly sophisticated answers to the question, "How do we monetize data?" πŸ§΅πŸ‘‡
The first answer to this question was the buzzword BIG DATA. People thought all you needed was a lot of data, didn't matter what kind, and it would basically monetize itself. Unfortunately, this was incorrect. So the next question became "How do we monetize lots of data?" 2/9
The answer to this question turned out to be the next buzzword. DATA SCIENCE. At this point, people still thought data was inherently easy to monetize so they figured anybody could do it. This turned out to be wrong as well. So the new question became... 3/9
"What kinds of data scientists are good at monetizing data?" The answer was UNICORNS. These were a kind of magical data scientist who were amazing at turning data into monetizable insights. The only problem is people weren't sure what unicorns looked like. 4/9
Some people thought data science unicorns were full stack data scientists, data scientists that could work at all stages of monetization, from exploration to deployment. Others thought unicorns might be T-shaped data scientists, generalists with a focused specialty. 5/9
The current consensus seems to be that data science unicorns, much like actual unicorns, either don't exist or if they do, are too rare to be the foundation of a successful business model. So now the question is "How do we monetize lots of data if unicorns don't exist?" 6/9
Let me answer that question with another question. What if the models could understand the data by themselves? This leads us to a new buzzword. DEEP LEARNING. Unfortunately, it turns out these models are tricky and require lots of skill and money to train. 7/9
So now, the question becomes, "How do I train lots and lots of models?" The answer is the current stage and the last and final buzzword. DATA ENGINEERING. This is where we are today. 8/9
So that's all we know so far about how you make a billion dollars with data:
- Get a lot of it
- Get some data scientists (unicorns if possible)
- Get some data engineers
- Train a lot of deep learning models
- ???
- $$$$$

9/9
If this thread helps you make a billion dollars, I don't ask for much just a 1% share...or if you like this kind of content and want to support it, follow me and like and retweet the thread!

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More from @kareem_carr

27 Mar
FOUR things to know about race and gender bias in algorithms:

1. The bias starts in the data

2. The algorithms don't create the bias but they do transmit it

3. There are a huge number of other biases. Race and gender bias are just the most obvious

4. It's fixable! πŸ§΅πŸ‘‡
By race and gender bias in algorithms, I mean the tendency for heavily data-driven AI algorithms to do things like reproduce negative stereotypes about women and people of color and to center white male subjects as normal or baseline. 2/9 ImageImage
While race and gender bias in algorithms *is fixable*, the current fixes aren't easy. They require us to understand and then mathematically model the processes that generate the biases in the data in the first place. 3/9
Read 13 tweets
25 Mar
Someone on Twitter just shared this very interesting essay. "Does A=A? I'm not so sure" by James Lindsay

It's a postmodernish musing on the truth of arithmetic statements! πŸ˜‚ I read it so you don't have to.

It disappeared while I was reading so this tweet is now the only copy!
I know it sounds like I'm making this up but this essay is gone like it never existed! The only reference I could find to the page on the internet is this comment on goodreads. goodreads.com/author_blog_po…
It may (or may not) surprise you to know that this man, James Lindsay, has mocked me mercilessly with all kinds of mean-spirited memes and sneering tweets for my philosophical musings about arithmetic. Portraying me as juvenile and dangerous.
Read 7 tweets
23 Mar
Are you interested in learning statistics or data analysis?

I think learning how to analyze data is tricky because it's actually 3 independent skills.

- Coding
- Applied Knowledge
- Probability Theory πŸ§΅πŸ‘‡
When I first started learning data analysis, it was frustrating for me to realize that being good at one of these skills didn't mean I was good at all of the others. So, If you've ever felt that way, you're not alone. 2/8
Coding: Being good at coding allows you to implement your ideas. While it's possible to get by using software, it will limit you as a data analyst. 3/8
Read 10 tweets
20 Mar
I think I may have a succulent addiction ...πŸ˜…
I like to re-pot them in fast draining soil.
I’m excited about this one which I think is a Lithops species of some kind. It’s my first.
Read 5 tweets
17 Mar
I gave up on talking about race on Twitter because I was having the same argument over and over again. In this thread, let me explain THE ANATOMY OF A TWITTER RACE ARGUMENT.
Whenever someone says "X is white supremacy" on Twitter where X is perfectionism or individualism or math worship, there are a constellation of reactions. Many of them predictable.
If X is a genuine point of division, it will often be the case that most white Americans tend to do and like X while most black Americans tend to dislike and not do X. This cultural difference may or may not be problematic.
Read 24 tweets
9 Mar
I've been in science for a while now and as far as I can tell, there are two types of people in this line of work. Those that think we should give everything to science and those that don't.
These two mindsets produce two types of work environments. I'll call them the results-first workplace and the people-first workplace.
In the people-first environment, they prioritize healthy work habits and relationships. Science is a critical piece of a whole and healthy life. In the results-first environment, all that matters is the outcome. People get the job done whatever the cost.
Read 17 tweets

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