Tina Huang explains in detail how to self study and learn technical things such as programming, data science, technical tools etc. She introduces a framework and exactly how to implement it as well as my method of learning most effectively and efficiently.
NVIDIA CEO Jensen Huang delivers the #GTC21 keynote, where he introduced a number of amazing breakthroughs.
Jupyter notebook is now one of the most popular tools for data scientists, even though it is fairly difficult to work with it in a team setting.
In this talk, explore how notebooks work under the hood and how we can build collaboration features to enable real-time editing.
Codebasics recently hosted this live stream is to provide career guidance to those who wants to transition to data science or programming career from non technical background.
Are you struggling with making good Power BI reports?
Adam from Guy in a Cube has 5 ideas to help you take your reports to the NEXT LEVEL! You don’t have to be a graphic artist to get there either.
Krish Naik recently hosted this live Q&A on data science.
Delta Lake is an open format storage layer that delivers reliability, security and performance on your data lake — for both streaming and batch operations.
By replacing data silos with a single home for structured, semi-structured and unstructured data, Delta Lake is the foundation of a cost-effective, highly scalable lakehouse.
Learn more: https://databricks.com/product/delta-lake-on-databricks
Karolina Sowinska discusses the most important topic in Machine Learning right now, namely model explainability.
It is one of the hottest discussion points in the data community, because ultimately if we cannot understand how the models arrive at the predictions, it renders them useless in many practical applications.
Simplify your data lake. Simplify your data architecture. Simplify your data engineering.
Powered by Delta Lake, Databricks combines the best of data warehouses and data lakes into a lakehouse architecture, giving you one platform to collaborate on all of your data, analytics and AI workloads.
Principal component analysis (PCA) is a workhorse algorithm in statistics, where dominant correlation patterns are extracted from high-dimensional data.
Steve Brunton explains it in this great video.
