Remove 2014 Remove Data Analysis Remove SQL
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Build generative AI applications quickly with Amazon Bedrock IDE in Amazon SageMaker Unified Studio

AWS Machine Learning Blog

They have structured data such as sales transactions and revenue metrics stored in databases, alongside unstructured data such as customer reviews and marketing reports collected from various channels. Use Amazon Athena SQL queries to provide insights.

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Analyzing the history of Tableau innovation

Tableau

Query allowed customers from a broad range of industries to connect to clean useful data found in SQL and Cube databases. The prototype could connect to multiple data sources at the same time—a precursor to Tableau’s investments in data federation. The Data Tab was added in v8.2

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How to optimize your LinkedIn as a Data Scientist?

Pickl AI

For example, if you are a Data Scientist, then you should add keywords like Python, SQL, Machine Learning, Big Data and others. These are some of the key skills that one needs to have if you are eyeing a fulfilling career as a Data Scientist. Highlight Your Experience Don’t miss this part. Wrapping it up !!!

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How to Use Exploratory Notebooks [Best Practices]

The MLOps Blog

And that’s what we’re going to focus on in this article, which is the second in my series on Software Patterns for Data Science & ML Engineering. I’ll show you best practices for using Jupyter Notebooks for exploratory data analysis. When data science was sexy , notebooks weren’t a thing yet. documentation.

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Analyzing the history of Tableau innovation

Tableau

Query allowed customers from a broad range of industries to connect to clean useful data found in SQL and Cube databases. The prototype could connect to multiple data sources at the same time—a precursor to Tableau’s investments in data federation. The Data Tab was added in v8.2

Tableau 98
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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. Data Engineering A job role in its own right, this involves managing the modern data stack and structuring data and workflow pipelines — crucial for preparing data for use in training and running AI models.

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How to Manage Unstructured Data in AI and Machine Learning Projects

DagsHub

Here’s the structured equivalent of this same data in tabular form: With structured data, you can use query languages like SQL to extract and interpret information. In contrast, such traditional query languages struggle to interpret unstructured data. This text has a lot of information, but it is not structured.