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Articles by Nathaniel DiRenzo
Analytics with Nathaniel DiRenzo
Analytics has become the cornerstone of decision-making across industries in today’s data-driven landscape. From healthcare to finance, gathering, analyzing, and deriving insights from vast amounts of data is essential. Data expert Nathaniel DiRenzo discusses the evolution of analytics, tracing its journey from simple data reporting to sophisticated predictive models.
Deploying ML Models using Streamlit
Quickly build and deploy a Python app with XGBoost & Streamlit GitHub Repo Streamlit App Let’s be honest: it’s hard to get the average person excited about data science. Even in professional settings it can be an uphill battle to get buy-in from decision-makers if they aren’t sold on the potential benefits of your work.
Beginner’s Guide to Machine Learning with Big Data
Now that we have a conceptual understanding of Spark and Google Cloud Storage, let’s put them both to work for us in a simple demonstration. Below, I’m going to walk through the process of cleaning a large dataset stored in Google Cloud Storage, and building a gradient-boosted regression model using Spark. But before I get into that, let’s go through the pre-requisite steps we need to complete before we can start coding.
Mitigating DDoS attacks with Classification Models
Using Dask, XGBoost, and Stacked Ensembling to defend against a ubiquitous form of cyberattack GitHub Repo Presentation PDF Module #4 of Metis’ Data Science and Machine Learning bootcamp has officially wrapped up! This module focused on Classification, a subset of Supervised Learning. And for our final project, we were asked to identify a problem that could be solved using classification methods, and assess the viability of solving that problem with classification models we produced.
Effectively Scoping and Communicating Data Science to Decision Makers
Photo by Carlos Muza on Unsplash Module #3 of Metis’ Data Science and Machine Learning bootcamp is all wrapped up! For this section, we switched things up a bit —took a (relative) break from a month of nonstop coding and zeroed in on Business Fundamentals, which provided an overview of the professional environments we will be putting our newfound skills to practice in, and how best to communicate insights generated with data to nontechnical stakeholders and decision makers.
Visualizing NYC’s Subway Traffic & Census Data
Photo by Mitchell Trotter on Unsplash The first module of Metis’ Data Science and Machine Learning Bootcamp is in the books & it’s been a whirlwind couple of weeks! Module #1 covered Exploratory Data Analysis, and for the associated project we were tasked with formulating a use case for turnstile data taken from the New York City MTA, and conducting an analysis to generate insights from the data.
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