Remove 2014 Remove Data Preparation Remove SQL
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How to Use Exploratory Notebooks [Best Practices]

The MLOps Blog

In 2014, Project Jupyter evolved from IPython. Example template for an exploratory notebook | Source: Author How to organize code in Jupyter notebook For exploratory tasks, the code to produce SQL queries, pandas data wrangling, or create plots is not important for readers. documentation. For one, Git diffs within.py

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Must-Have Prompt Engineering Skills for 2024

ODSC - Open Data Science

GANs, introduced in 2014 paved the way for GenAI with models like Pix2pix and DiscoGAN. Databricks: Powered by Apache Spark, Databricks is a unified data processing and analytics platform, facilitates data preparation, can be used for integration with LLMs, and performance optimization for complex prompt engineering tasks.

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dplyr

Dataconomy

Dplyr is an essential package in R programming, particularly beneficial for data manipulation tasks. It streamlines data preparation and analysis, making it easier for data scientists and analysts to extract insights from their datasets. dbplyr : Allows dplyr functions to interface with SQL databases.