Remove Big Data Remove Data Collection Remove Predictive Modeling Remove Unstructured Data
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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Data science is an area of expertise that combines many disciplines such as mathematics, computer science, software engineering and statistics. It focuses on data collection and management of large-scale structured and unstructured data for various academic and business applications.

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What is data science? Transforming data into value

CIO Business Intelligence

Data science is a method for gleaning insights from structured and unstructured data using approaches ranging from statistical analysis to machine learning. Data science gives the data collected by an organization a purpose. Data science certifications. Data science teams.

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How to supercharge data exploration with Pandas Profiling

Domino Data Lab

Producing insights from raw data is a time-consuming process. Predictive modeling efforts rely on dataset profiles , whether consisting of summary statistics or descriptive charts. Results become the basis for understanding the solution space (or, ‘the realm of the possible’) for a given modeling task.

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The most valuable AI use cases for business

IBM Big Data Hub

By infusing AI into IT operations , companies can harness the considerable power of NLP, big data, and ML models to automate and streamline operational workflows, and monitor event correlation and causality determination. AIOps is one of the fastest ways to boost ROI from digital transformation investments.

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What is a Data Pipeline?

Jet Global

Machine Learning Pipelines : These pipelines support the entire lifecycle of a machine learning model, including data ingestion , data preprocessing, model training, evaluation, and deployment. API Data Pipelines : These pipelines retrieve data from various APIs and load it into a database or application for further use.

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Leveraging user-generated social media content with text-mining examples

IBM Big Data Hub

Information retrieval The first step in the text-mining workflow is information retrieval, which requires data scientists to gather relevant textual data from various sources (e.g., The data collection process should be tailored to the specific objectives of the analysis.