Video Highlights: Technical Intro to Transformers and LLMs — with Kirill Eremenko

In this video presentation, our good friend Jon Krohn, Co-Founder and Chief Data Scientist at the machine learning company Nebula, is joined by Kirill Eremenko to explore what goes into well-crafted LLMs, what makes Transformers so powerful, and how to succeed as a data scientist in this new age of generative AI.

Data Science 101: The Data Science Venn Diagram

Welcome to insideBIGDATA’s Data Science 101 channel bringing you perspectives for the topics of the day in data science, machine learning, AI and deep learning. Many of the video presentations come from my lectures for my Introduction to Data Science class I teach at UCLA Extension. In today’s slide-based video presentation I discuss The Data Science Venn Diagram, a subject-by-subject overview of the constituent parts of the discipline of data science.

Video Highlights: Copilot for R

Our video highlights selection for today is by data science industry luminary David Smith who made a presentation to the NYC Data Hackers on the topic of Copilot for R. If you haven’t come across Copilot before, it’s like an AI-based pair programmer that suggests new lines of code, and perhaps entire functions, based on context.

Data Science 101: The Data Science Process

Welcome to insideBIGDATA’s Data Science 101 channel brining you perspectives for the topics of the day in data science, machine learning, AI and deep learning. Many of the video presentations come from my lectures for my Introduction to Data Science class I teach at UCLA Extension. In today’s slide-based video presentation I discuss The Data Science Process, an overview of the steps that data scientists use solving problems with data science and machine learning technologies.

Video Highlights: FeatureTerminatoR Package for R

FeatureTerminatoR is an R package to remove unimportant variables from statistical and machine learning models automatically. The motivation for this package is simple, while there are many packages that do similar things, few of them perform automated removal of the features from your models. The author provides the video presentation below to help get you familiar with how the package works.

Mistakes to Avoid When Starting a Career in Data Science

In this contributed article, IT and digital marketing specialist Natasha Lane, highlights how the shortage of data science talent is dramatic, but there are still a few mistakes you can make getting your foot in the door. These are the types of mistakes that can slow down your initial career progress, so the article covers them to help you make sure you’ll avoid the pitfalls.

UCLA DataFest Winners Announced, Presentations Posted

For the annual UCLA DataFest, student worked hard with data pertaining to the monumental challenge we are all facing: COVID-19. This year’s virtual version of ASA DataFest at UCLA brought forth unforeseen challenges and wonderful opportunities. This beloved tradition is generally a competition wherein groups of three to five students have just 48 hours to make sense of a huge data set and present their findings in five minutes, using just two slides.

Be on Top of Key Data Analytic Trends

Emily Washington: ‘Businesses are increasingly evaluating ways to streamline their overall technology stack… to successfully leverage big data and analytics’. Tech trends in data analytics are seeing the industry soar. Discover more here.

Data Science 101: Handling Missing Data (Revisited)

I recently received the following question on data science methods from an avid reader of insideBIGDATA who hails from Taiwan. I think the topics are very relevant to many folks in our audience so I decided to run it here in our Data Science 101 channel. The issue of missing data is one most data scientists see quite frequently.

How Computers Learn

This Vienna Gödel Lecture provides a fascinating talk by Peter Norvig, Research Director at Google Inc. in the field of intelligent computers. Norvig talks about his long experience in AI and Machine Learning. The talk explains how computers learn from examples and what are the promises and limitations of these techniques.