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The Rise of Unstructured Data

Cloudera

In terms of representation, data can be broadly classified into two types: structured and unstructured. Structured data can be defined as data that can be stored in relational databases, and unstructured data as everything else. Here we briefly describe some of the challenges that data poses to AI.

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Flight Price Predictor: Training Models to Pinpoint the Best Time for Booking

AltexSoft

But nothing is impossible for people armed with intellect and algorithms. Flight dataset structure. To get an idea of how to structure data for airfare prediction, let’s take a look at the above-mentioned Kaggle’s training dataset, which contains over 10,000 records about flights executed between March and June 2019.

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The Importance of Python in Data Science and Machine Learning

U-Next

In 2019, Python was the fastest-growing major programming language. . Python has several benefits for Data Scientists and Machine Learning experts. This is due in part to Python’s efficient data structures and algorithms. Python is used by many large companies such as Google, Instagram, Spotify, and Netflix.

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Using Graph Processing for Kafka Stream Visualizations

Confluent

We will cover how you can use them to enrich and visualize your data, add value to it with powerful graph algorithms, and then send the result right back to Kafka. Instead of storing tables and columns, Neo4j represents all data as a graph, meaning that the data is a set of nodes with labels and relationships.

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Popular Use Cases for Real-Time Analytics

Rockset

By 2019, 65% of Dominos’ sales came through digital channels including home devices and emoji texts, reimagining the brand for the digital era. The latest data is fed into an algorithm that spits out the live order status to pizza lovers. The Dominos’ Pizza Tracker is the quintessential example of real-time analytics.

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How BlaBlaCar Built a Practical Data Mesh to Support Self Service Analytics at Scale

Monte Carlo

The data organization at BlaBlaCar makes sure data flows accurately to consumers, product managers, operations teams, marketing teams, and customer support. They also build and productionalize algorithms that automate decision-making. Kineret agreed, telling us, “We don’t make any decision without consulting data.”

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Natural Language Processing in Healthcare: Using Text Analysis for Medical Documentation and Decision-Making

AltexSoft

Its deep learning natural language processing algorithm is best in class for alleviating clinical documentation burnout, which is one of the main problems of healthcare technology. Unstructured data is unavoidable, yet extremely valuable. However useful, CDSSs are mostly limited to processing only structured data.

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