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My methods of using GPT-4 to optimize my datavisual workflow For the past 6 months, I have been a heavy daily GPT-4 user. I know it makes me sound like an addict, but I have found many good reasons and many excellent use cases where this tool is an invaluable resource for improving my data workflow.
Photo by Juraj Gabriel on Unsplash Dataanalysis is a powerful tool that helps businesses make informed decisions. In this blog, we’ll be using Python to perform exploratory dataanalysis (EDA) on a Netflix dataset that we’ve found on Kaggle. df['rating'].replace(np.nan, value_counts()[:20].plot(kind="bar",color="Blue")
After the first 10 testing submissions, I realised that there was a concept drift happening between 2007 and 2008. To me, this probably means, the decision rules for grant applications were somehow changed during 2007 and 2008. What tools I used Software/Tools used for modelling and dataanalysis: Weka 3.7.1
Four reference lines on the x-axis indicate key events in Tableau’s almost two-decade history: The first Tableau Conference in 2008. The first Tableau customer conference was in 2008. Tableau had its IPO at the NYSE with the ticker DATA in 2013. Visual encoding, in particular, tapped the power of the human visual system.
Four reference lines on the x-axis indicate key events in Tableau’s almost two-decade history: The first Tableau Conference in 2008. The first Tableau customer conference was in 2008. Tableau had its IPO at the NYSE with the ticker DATA in 2013. Visual encoding, in particular, tapped the power of the human visual system.
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Large language models (LLMs) can help uncover insights from structured data such as a relational database management system (RDBMS) by generating complex SQL queries from natural language questions, making dataanalysis accessible to users of all skill levels and empowering organizations to make data-driven decisions faster than ever before.
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