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Innovations in Analytics: Elevating Data Quality with GenAI

Towards AI

Last Updated on October 31, 2024 by Editorial Team Author(s): Jonas Dieckmann Originally published on Towards AI. Data analytics has become a key driver of commercial success in recent years. The ability to turn large data sets into actionable insights can mean the difference between a successful campaign and missed opportunities.

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What is Data Pipeline? A Detailed Explanation

Smart Data Collective

Data pipelines automatically fetch information from various disparate sources for further consolidation and transformation into high-performing data storage. There are a number of challenges in data storage , which data pipelines can help address. Choosing the right data pipeline solution.

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Build Data Pipelines: Comprehensive Step-by-Step Guide

Pickl AI

Summary: This blog explains how to build efficient data pipelines, detailing each step from data collection to final delivery. Introduction Data pipelines play a pivotal role in modern data architecture by seamlessly transporting and transforming raw data into valuable insights.

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10 Technical Blogs for Data Scientists to Advance AI/ML Skills

DataRobot Blog

These data science teams are seeing tremendous results—millions of dollars saved, new customers acquired, and new innovations that create a competitive advantage. Other organizations are just discovering how to apply AI to accelerate experimentation time frames and find the best models to produce results. Read the blog. Read the blog.

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How Dataiku and Snowflake Strengthen the Modern Data Stack

phData

It must integrate seamlessly across data technologies in the stack to execute various workflows—all while maintaining a strong focus on performance and governance. Two key technologies that have become foundational for this type of architecture are the Snowflake AI Data Cloud and Dataiku. Let’s say your company makes cars.

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AI in Time Series Forecasting

Pickl AI

Summary: AI in Time Series Forecasting revolutionizes predictive analytics by leveraging advanced algorithms to identify patterns and trends in temporal data. By automating complex forecasting processes, AI significantly improves accuracy and efficiency in various applications. billion by 2030. What is Time Series Forecasting?

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Big Data vs. Data Science: Demystifying the Buzzwords

Pickl AI

This crucial step involves handling missing values, correcting errors (addressing Veracity issues from Big Data), transforming data into a usable format, and structuring it for analysis. This often takes up a significant chunk of a data scientist’s time. Think graphs, charts, and summary statistics.