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Use of AI and Big Data Analytics to Manage Pandemics

Pickl AI

Summary: This blog examines the role of AI and Big Data Analytics in managing pandemics. It covers early detection, data-driven decision-making, healthcare responses, public health communication, and case studies from COVID-19, Ebola, and Zika outbreaks, highlighting emerging technologies and ethical considerations.

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Data lakes vs. data warehouses: Decoding the data storage debate

Data Science Dojo

By using this method, you may speed up the process of defining data structures, schema, and transformations while scaling to any size of data. Through data crawling, cataloguing, and indexing, they also enable you to know what data is in the lake. References: Data lake vs data warehouse

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5 Current Trends in Big Data for 2022 and Beyond

Smart Data Collective

We should expect to analyze big data in the future as businesses are looking more closely to use it to remain competitive. This post outlines five current trends in big data for 2022 and beyond. The Rise of Streaming Analytics.

Big Data 145
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Invest In Analytics To Improve Your Production Line

Smart Data Collective

Investing in analytics isn’t something to take lightly, but companies that do it well can set themselves up for success they didn’t even know was attainable. Who’s Using Analytics in Manufacturing? Broadly speaking, big data analytics is your company’s ticket to efficiency and productivity improvements.

Analytics 104
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Navigating the 2024 Data Analyst career growth landscape

Pickl AI

Summary: The blog delves into the 2024 Data Analyst career landscape, focusing on critical skills like Data Visualisation and statistical analysis. It identifies emerging roles, such as AI Ethicist and Healthcare Data Analyst, reflecting the diverse applications of Data Analysis.

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Splunk Tutorial For Beginners: It’s Application & Features

Pickl AI

Data Analysis is significant as it helps accurately assess data that drive data-driven decisions. Different tools are available in the market that help in the process of analysis. It is a powerful and widely-used platform that revolutionises how organisations analyse and derive insights from their data.

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The Age of Health Informatics: Part 1

Heartbeat

Image from "Big Data Analytics Methods" by Peter Ghavami Here are some critical contributions of data scientists and machine learning engineers in health informatics: Data Analysis and Visualization: Data scientists and machine learning engineers are skilled in analyzing large, complex healthcare datasets.