The nature of an asset’s liquidity is critical to understand for an investor, and it’s no different in the decentralized world of finance & investment.
As well, liquidity is a critical factor to the long-term success of exchanges, which help make up the infrastructure for buying & sharing crypto assets.
But what is liquidity, exactly?
And what’s the state of liquidity as it relates to DeFi, a crypto segment within which — as of today — more than $42 Billion USD is currently locked?
Two of the most important components to analyzing and forecasting with Time Series data are plotting — and reviewing— the Autocorrelation and Partial Autocorrelation functions.
Understanding the difference between these two calculations is the focus of this post, with the goal being to provide solid answers to the following:
OK, let’s dive in.
It’s useful to mention here that statistical correlation in general helps us to identify the…
One of the most critical elements to the success of nearly any cryptocurrency or DeFi endeavor is in how it approaches tokenomics.
It’s useful to think of tokenomics as the monetary policy of the crypto world.
It can impact user adoption, token valuation and more.
In much the same way a country’s monetary policy impacts the usage, value and more of its currency.
In this post, we’ll discuss the token distribution and (launch period) rewards system for BIOPset, the world’s first decentralized binary options trading platform offering instant access and no holds on speculator funds.
(It’s underlying asset is Ethereum…
After completing my most recent data science project, where I was asked to create a classification model, I thought it would be helpful to create a reference guide on the Confusion Matrix, with a focus on the important terms relating to this highly-important tool.
Throughout the modeling process — whether KNN, Decision Trees, Random Forests, XGBoost, etc. — I found myself relying on the Confusion Matrix for a simple & sound way to measure the performance of a respective algorithm.
Indeed, there are more ways to measure performance, but for this post I’ll be focusing on the Confusion Matrix.
The frequent practice of fitting the final selected model followed by reporting estimates and confidence intervals without adjusting them to take the model building process into account has led to calls to stop using stepwise model building altogether or to at least make sure model uncertainty is correctly reflected. — from Wikipedia
NOTE: If you want to get right into the Stepwise addition questions, please scroll down a bit :)
While in my part-time online data science program through Flatiron School, it’s been both challenging and rewarding to complete my first full, realish-life, and professional data science project.
Decentralized Finance, or DeFi, is a (not so) new development in the crypto world, and if you’re reading this, you’re probably wondering about it.
Simply put, you want to understand just what the heck it is.
Hopefully this toe-dipping guide helps.
*** You won’t find any investment advice here, however.***
The goal of this post is to attempt (!) to answer the following questions (for you and for me) so as to educate ourselves about the wonderful (?) world of decentralized finance:
“When all you have is a hammer, everything looks like a nail.” — Abraham Maslow
Here’s what I’m thinking these days:
Beyond so many other definitions and/or descriptions, data science is a tool.
But, it’s also a toolbox filled with tools.
One of these tools is Python.
It’s one of the best tools to have in one’s data science toolbox.
But, Python is also both tool and toolbox.
And even though my data science learning journey is still just beginning, I’m realizing how important one of these particular tools is which can be found inside the (well-equipped) Python toolbox.
2.5 quintillion bytes of data were created every single day back in 2018, with this number having grown since then.
I’d say this is all the reason one needs to study data science.
Data is everywhere.
But I have other reasons.
Back in 2017, after four years of teaching ESL in South Korea, I decided it was time to transition into work I felt was more aligned with the impact I wanted to have going forward.
(The priceless nature of these four years is beyond the scope of this piece, but just know, deciding to teach ESL abroad changed the…
The majority of cryptocurrencies are built on the blockchain. In this article, I discussed why one ought to be more excited about blockchain — and not bitcoin — due to the power of distributed ledger technology. Blockchain is just one example of distributed ledger technology.
Hashgraph is another, and it might be the biggest threat to the blockchain.
Here, a description of Hedera hashgraph, from their website:
“The Hedera hashgraph platform provides a new form of distributed consensus; a way for people who don’t know or trust each other to securely collaborate and transact online without the need for a…
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