Python

Datacast Episode 130: Towards Accessible Data Analysis with Emanuel Zgraggen

Datacast Episode 130: Towards Accessible Data Analysis with Emanuel Zgraggen

Before founding Einblick in 2020, Emanuel was a postdoc in the database group at MIT and got his Ph.D. in Computer Science from Brown University. He worked on various interactive tools for visual data exploration and analysis during this time. Most of them either influenced or are direct predecessors of Einblick.

Before coming to the US, Emanuel worked as a Software and Data Engineer for various financial companies in Zurich. He tried his luck as a freelancer building in-studio touchscreen installations for Swiss National TV and developing a Spotify clone that failed miserably.

Datacast Episode 111: Astrophysics, Visualization Recommendation, and Scalable Data Science with Doris Lee

Datacast Episode 111: Astrophysics, Visualization Recommendation, and Scalable Data Science with Doris Lee

Doris Lee is the co-founder and CEO of Ponder, a startup delivering scalable, enterprise-ready pandas that improve the productivity of data teams. She graduated with her Ph.D. from UC Berkeley RISE Lab in 2021, where she developed data science tools to accelerate insight discovery.

Datacast Episode 91: Collaborative Data Workspace, The Sharing Gap, and Engineering Management with Caitlin Colgrove

Datacast Episode 91: Collaborative Data Workspace, The Sharing Gap, and Engineering Management with Caitlin Colgrove

Caitlin Colgrove is the Co-Founder and CTO of Hex Technologies, a collaborative data workspace for building and sharing data projects using SQL and Python. Caitlin has spent her career as a software engineer building data analytics tools, first at Palantir and then later at startups including Remix and Hex. As a CTO, her focus has expanded from purely technology to growing and developing diverse and inclusive engineering teams.

Recommendation System Series Part 4: The 7 Variants of Matrix Factorization for Collaborative Filtering

Recommendation System Series Part 4: The 7 Variants of Matrix Factorization for Collaborative Filtering

In this post and those to follow, I will be walking through the creation and training of recommendation systems, as I am currently working on this topic for my Master Thesis. Part 4 looks into the nitty-gritty mathematical details of matrix factorization, arguably the most common baseline model for recommendation system research these days.

A Friendly Introduction to Open Source Data Science for Business Leaders

A Friendly Introduction to Open Source Data Science for Business Leaders

Open source is a key enabler for enterprise data science, both in terms of the growing ecosystem of open-source tools and the expanding number of complementary enterprise data science platforms that incorporate and build on open source languages and tools. The challenge is identifying which of those tools is relevant and valuable to your business. Assessing the maturity of these projects, grappling with any licensing issues, and making sure your team has the correct skillset to use them are challenges that many companies are now facing.