In the previous post, I explored how useful ChatGPT currently is for data visualisation. This was done through testing this AI tool on a number of tasks and on its information retrieval ability. The results showed that ChatGPT still had some way to go on the theory and consulting side of things, as it still made mistakes and had limited knowledge on the subject. Despite this, it's still impressive how it can easily provide a written response that was mostly correct for what I had asked. Where ChatGPT really shined was on the coding side of DataViz.

This book aims to get you up to speed with what is, in my opinion, the most powerful data visualization stack going: Python and JavaScript. You'll learn enough about big libraries like Pandas and D3 to start crafting your own web data visualizations and refining your own toolchain. Expertise will come with practice, but this book presents a shallow learning curve to basic competence. If you're reading this, I'd love to hear any feedback you have. Please post it to pyjsdataviz@kyrandale.com. You'll also find a working copy of the Nobel visualization the book literally and figuratively builds toward at http://kyrandale.com/static/pyjsdataviz/index.html. The bulk of this book tells one of the innumerable tales of data visualization, one carefully selected to showcase some powerful Python and JavaScript libraries and tools which together form a toolchain. This toolchain gathers raw, unrefined data at its start and delivers a rich, engaging web visualization at its end. Like all tales of data visualization, it is a tale of transformation--in this case, transforming a basic Wikipedia list of Nobel Prize winners into an interactive visualization, bringing the data to life and making exploration of the prize's history easy and fun. A primary motivation for writing the book is the belief that, whatever data you have and whatever story you want to tell with it, the natural home for the visualizations you transform it into is the Web.

I'm reading "Artificial Intelligence -- The Revolution Hasn't Happened Yet" by Berkeley computer science professor Michael I Jordan. It is a quick, light, read but thought provoking. His point is that once the dust settles after the hype around ubiquitous Artificial Intelligence (AI), we'll realize that what is being called AI is less general purpose intelligence than a collection of powerful tools for augmenting human intelligence (e.g., Siri, what he calls intelligence augmentation or IA) and the intelligent infrastructure (II) that makes these tools possible. There are also some good historical anecdotes in the article. Whether or not you agree with Jordan's point in this piece, I'd still encourage you to check out the reading list he's long suggested to his post docs and grad students, it is a gem.

Today, with Digitization of everything, 80 percent the data being created is unstructured. Audio, Video, our social footprints, the data generated from conversations between customer service reps, tons of legal document's texts processed in financial sectors are examples of unstructured data stored in Big Data. Click here to read more. What is Going on with Residential IoT Cyber Security? For sure you have heard about the recent DDoS attacks that occurred last October 21st on Dyn's DNS service. The news broke out reporting that many well-known Internet services were not available. According to Hacker News Twitter, Etsy, Spotify and other sites were affected.

Today, with Digitization of everything, 80 percent the data being created is unstructured. Audio, Video, our social footprints, the data generated from conversations between customer service reps, tons of legal document's texts processed in financial sectors are examples of unstructured data stored in Big Data. Click here to read more. What is Going on with Residential IoT Cyber Security? For sure you have heard about the recent DDoS attacks that occurred last October 21st on Dyn's DNS service. The news broke out reporting that many well-known Internet services were not available. According to Hacker News Twitter, Etsy, Spotify and other sites were affected.

There are a number of visualizations that frequently pop up in machine learning. Scikit-plot is a humble attempt to provide aesthetically-challenged programmers (such as myself) the opportunity to generate quick and beautiful graphs and plots with as little boilerplate as possible.

Today, with Digitization of everything, 80 percent the data being created is unstructured. Audio, Video, our social footprints, the data generated from conversations between customer service reps, tons of legal document's texts processed in financial sectors are examples of unstructured data stored in Big Data. Click here to read more. What is Going on with Residential IoT Cyber Security? For sure you have heard about the recent DDoS attacks that occurred last October 21st on Dyn's DNS service. The news broke out reporting that many well-known Internet services were not available. According to Hacker News Twitter, Etsy, Spotify and other sites were affected.

Great list of resources: data science, visualization, machine learn... Want to know how to choose Machine Learning algorithm? Great list of resources: data science, visualization, machine learn... Want to know how to choose Machine Learning algorithm?

Overcome the Analytic Limitations of Access and Excel - Although they are often a cornerstone of a company's analytic toolkit, tools like Access and Excel are designed for data storage and basic analytics, not for creating the complex analytics that are required by today's fast-moving businesses. New technologies can help organizations move past the analytic limitations of Access and Excel, especially when dealing with the demands of processing more data, and making analytics available to more decision makers and stakeholders – all at greater speed than ever before. Overcome the Analytic Limitations of Access and Excel - Although they are often a cornerstone of a company's analytic toolkit, tools like Access and Excel are designed for data storage and basic analytics, not for creating the complex analytics that are required by today's fast-moving businesses. New technologies can help organizations move past the analytic limitations of Access and Excel, especially when dealing with the demands of processing more data, and making analytics available to more decision makers and stakeholders – all at greater speed than ever before.

Whitepaper: The Journey to Open Data Science - Migrating from traditional analytics to modern Open Data Science (ODS) is one of the most important trends of the decade. However, although this migration is critical to the core mission of many enterprises, practicing Data Science effectively has remained a thorny challenge.