The last few weeks, people have been asking me a lot about how to become a company that can leverage Machine Learning and AI. This can be a complex issue and hence, difficult to explain. So, I decided to take everything I learned about Organizational Behavior and Culture and all I know about Data Science to try and come up with a good and simple explanation.
Data today has become the most important resource of any organization, at least for those who expect to still be in the market in just a few years. Although we hear many companies constantly…
You might be thinking, I already know programming in Python, and I am absolutely amazing at Pandas. Why would I need to learn another language such as SQL?
Simple, because most of existing structured data in the world right now is stored in Relational Databases, you know, the stuff we know as tables that relate to each other, and most of those tables designed, created and run-on Structured Query Language, or SQL (also known as “seequel” for those of us who don’t like acronyms or spelling).
I know, shocking, after so many times converting .csv files into dataframes for learning…
A few days ago, I finished the 66th day of my second round of the #66DaysOfData challenge. To celebrate, I wanted to do a recap of an extremely exciting round, filled with projects, ideas, events and, most of all, great prospects for the future.
Let me start by understanding the differences between the first round and this one. Last year, when Ken Jee started this challenge, I took it as a way to build the specific habit of learning and/or practicing something related to Data Science every single day. From taking a course, solving a challenge, reading an article, watching…
Machine Learning is an amazing technology. Unfortunately, its hype, like with everything that becomes “cool” or “in”, is starting to create a snake oil market full of smoke and mirrors, with potentially catastrophic consequences.
Apologies for the apocalyptic start to the article, but it is not wrong. Let me give you a little bit of background so we can dive into the details of the problem I am talking about.
Many, many (probably one more many) years ago, before I even knew what Machine Learning was (or even before more than a handful of people knew it existed), I studied…
Today, the Road to Data Science takes a different turn, instead of another story about my experiences, so you can have a smoother journey, I wanted to talk about where I believe data science, and mostly data scientists should be going.
First off, a bit of background. As some of you may know, I am psychologist, specializing in behavioral economics and psychometry, but what most of you probably do not know, is that I have spent a lot of time the past 10 years working closely with many entrepreneurs as an entrepreneurship and innovation professor, consultant, and mentor. …
Previously, we created an Attrition Risk formula. Now it is time to build a model with it.
We will keep it simple to start with and build from there in future articles, so it does not become overwhelming. Like always we start by importing the libraries we will need, in this case, NumPy and Pandas will suffice:
On the last story, I mentioned some of my favorite python libraries and why I believe they are important. For some of you, who are already well in your journey to learn data science, you may have noted I left out one of the most important ones out: matplotlib. It was not a mistake, it was completely on purpose, simply because it deserves its own story, and here it is.
In other words, we experience the world mainly through our eyes, meaning that when we try to understand something, having a visual representation of it gets us a long way…
Last time we discussed how a biostatistics risk formula is built using the Lung Cancer Risk for smokers built by Maria Maraki. Now, it is time to start building our own risk model for attrition based on the OCEAN personality model. To do this, we need to take all the info we can from the available research papers.
Since we will want to use this formula to later build a formula-based prediction model, then why not do it with a Jupyter Notebook.
As usual, we set up our notebook with the libraries we believe will be useful, this time around…
On my last article from a couple of days back, I decided to start by building a risk formula using the information from the original studies we found, others I have spent the last days reading (and will share where I can in this article. The reason to do this, for me, is that this allows me to go deeper into the possible reality of being able to predict attrition from personality (using the OCEAN model in this case), using what scientist had to do before the existence of what we now refer to as Data Science.
How is a…
You are already a Pythonista (or like me at this point, you think you are and will eventually discover you still have a LOT to learn, but don’t worry too much about it, it happens to most of us, it is called the Dunning–Kruger effect), and are now ready to get to the next level.
You could simply go right ahead into building ML models or jump into deep learning and get confused between Convolutional Neural Networks and Adversarial Neural Networks as well as stumble into a Sigmoid and fall right through a Leaky ReLU. …
Jack Raifer Baruch
Data Science / Behavioral Economics / Machine Learning / Python / Artificial Intelligence / Neural Networks / Video Games
