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How AWS sales uses Amazon Q Business for customer engagement

AWS Machine Learning Blog

We work backward from the customers business objectives, so I download an annual report from the customer website, upload it in Field Advisor, ask about the key business and tech objectives, and get a lot of valuable insights. He has worked on a variety of products, including data visualization tools and mobile applications.

AWS 98
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A Few Proven Suggestions for Handling Large Data Sets

Smart Data Collective

You can finally understand what you’re looking at and what the data is saying. The format can be classified by size, but you can choose to organize data horizontally or vertically/by column. It doesn’t matter if you use graphs or charts, you need to get better at data visualization. Speaking of which.

Database 130
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5 Analytic Tools Companies Use To Organize and Study their Data

Smart Data Collective

It can connect any data source, although the better your source, the better the results will be. The software is easy to use and provides the ability to download different file formats. Another key benefit is that it allows companies to create data visualizations! Choosing an Analytics Tool.

Analytics 132
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How to Migrate Paginated Reports (SSRS) to Power BI Service with Snowflake

phData

The migration of SSRS (SQL Server Reporting Services) reports to Power BI Service marks a significant shift in data visualization and reporting capabilities. During the migration process, existing.rdl reports are pointed to a Snowflake Data Cloud database from an existing data source in Power BI Report Builder.

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Prepare training and validation dataset for facies classification using Snowflake integration and train using Amazon SageMaker Canvas

AWS Machine Learning Blog

Upload facies CSV data to Snowflake In this section, we take two open-source datasets and upload them directly from our local machine to a Snowflake database. Download the training_data.csv and validation_data_nofacies.csv files to your local machine. Note that the schema is a little different to the training data.

ML 93
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Getting Your First Job in Data Science

Data Science 101

Among the skills necessary to become a data scientist include an analytical mindset, mathematics, data visualization, and business knowledge, just to name a few. In addition to having the skills, you’ll need to then learn how to use the modern data science tools. How to get a Data Science Job.

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Ultimate Pandas Cheat Sheet : Mastering Pandas

Pickl AI

It offers a collection of key commands and functions for efficient data manipulation using the Pandas library in Python. From reading data in various formats like CSV, Excel, and SQL to filtering, sorting, and aggregating data, this cheat sheet covers essential operations. Think of it as a spreadsheet or SQL table.