Non-Brand Data

“Non-Brand Data” delves into the realm of enterprise data management and AI applications, focusing on the challenges and solutions related to leveraging data beyond traditional branding contexts. The document explores topics such as the importance of understanding and utilizing diverse data sources, the pitfalls of treating data as static, and the necessity for AI systems to comprehend and interact with dynamic, living data. It also discusses the significance of secure knowledge base infrastructure, multi-tenancy architectures, and the role of technologies like Generative AI, Vector Embeddings, and Semantic Search in enhancing data understanding and retrieval processes.

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Building an Interactive Data Analysis Notebook With marimo and DuckDB

Non-Brand Data

Learn to build modern dashboard in a simple way

Building a Multi-Asset Market Monitor with TickerLayer

Non-Brand Data

Understand market data and learn how to work with it in Python

Getting Started With TimesFM-3 for Time-Series Forecasting

Non-Brand Data

Learn how to use TimesFM-3 for time-series prediction with a Python example.

Can TabPFN-3.5 Compete With Traditional Machine Learning Models?

Non-Brand Data

Compare four models on two datasets to understand their prediction performance and runtime.

Module 14: Writing the results and recommendations

Non-Brand Data

A SQL result can be correct and still make a weak report. The table gives the numbers, but the reader still needs to know what they mean and what they don't show.

Module 13: When a SQL query needs optimization

Non-Brand Data

In this module, we will learn how to read a PostgreSQL query plan and decide whether an index is worth keeping.

Module 12: Checking whether your result is right

Non-Brand Data

Let's learn how to check a SQL result before we use it in a report or recommendation.

Module 11: Using window functions without changing the row count

Non-Brand Data

Let’s learn how to calculate totals, running values, and ranks while keeping every order row in the result.

Module 10: Breaking a query into steps with subqueries and CTEs

Non-Brand Data

Let's learn how to split a long SQL query into named steps and check the result from each step.

Context Engineering for Data Science

Non-Brand Data

Helping AI solve the right data science problem

Module 9: When a join increases your numbers

Non-Brand Data

Let's learn why a one-to-many join can increase a total and how to keep the number correct.

Module 8: Choosing between INNER JOIN and LEFT JOIN

Non-Brand Data

Let's learn how `INNER JOIN`, `LEFT JOIN`, and filter placement change the rows in our result.