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Data Mesh Architecture on Cloud for BI, Data Science and Process Mining

Data Science Blog

Companies use Business Intelligence (BI), Data Science , and Process Mining to leverage data for better decision-making, improve operational efficiency, and gain a competitive edge. Process Mining offers process transparency, compliance insights, and process optimization.

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Understanding Predictive Analytics

Pickl AI

Summary: Predictive analytics utilizes historical data, statistical algorithms, and Machine Learning techniques to forecast future outcomes. This blog explores the essential steps involved in analytics, including data collection, model building, and deployment. What is Predictive Analytics?

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A Brief Introduction to Data Mining Functionalities

Pickl AI

Meta Description: Discover the key functionalities of data mining, including data cleaning, integration. Summary: Data mining functionalities encompass a wide range of processes, from data cleaning and integration to advanced techniques like classification and clustering.

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Enhancing Business Success: Exploring Key Analytical Capabilities

Pickl AI

Diagnostic Analytics Diagnostic analytics goes a step further by explaining why certain events occurred. It uses data mining , correlations, and statistical analyses to investigate the causes behind past outcomes. It analyses patterns to predict trends, customer behaviours, and potential outcomes.

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Why do I Need KNIME If I Have Snowflake?

phData

With a vast library of pre-built nodes, KNIME makes it easy to integrate data from various sources, perform data cleansing and transformations, and create reusable workflows. With KNIME, you can perform complex analytics tasks, build machine learning models, and generate predictions and recommendations.

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Exploring Different Types of Data Analysis: Methods and Applications

Pickl AI

Role in Extracting Insights from Raw Data Raw data is often complex and unorganised, making it difficult to derive useful information. Data Analysis plays a crucial role in filtering and structuring this data. Predictive Data Analysis Predictive Data Analysis uses historical data to forecast future trends and behaviours.

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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.