Dataconomy

APRIL 27, 2023

Conversely, OLAP systems are optimized for conducting complex data analysis and are designed for use by data scientists, business analysts, and knowledge workers. OLAP systems support business intelligence, data mining, and other decision support applications.

Database 159

Database Data Scientist Data Mining Data Mining 159

ODSC - Open Data Science

FEBRUARY 8, 2023

BigQuery operation principles Business intelligence projects presume collecting information from different sources into one database. Then, an analyst prepares them for reporting (via data visualization tools like Google Data Studio). You only pay for the resources you use.

Database 97

Database Data Science Big Data Big Data 97

Dataconomy

JUNE 13, 2023

At its core, decision intelligence involves collecting and integrating relevant data from various sources, such as databases, text documents, and APIs. This data is then analyzed using statistical methods, machine learning algorithms, and data mining techniques to uncover meaningful patterns and relationships.

Power BI 103

Power BI Data Analysis Data Analysis Artificial Intelligence 103

Pickl AI

SEPTEMBER 12, 2024

Summary : This article equips Data Analysts with a solid foundation of key Data Science terms, from A to Z. Introduction In the rapidly evolving field of Data Science, understanding key terminology is crucial for Data Analysts to communicate effectively, collaborate effectively, and drive data-driven projects.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

Pickl AI

JULY 18, 2023

SAS: Analytics and Business Intelligence SAS is a leading programming language for analytics and business intelligence. It provides a comprehensive suite of tools for data manipulation, statistical analysis, and predictive modeling. Q: What role does SAS play in Data Science?

Data Science 52

Data Science SQL Data Scientist Python 52

Pickl AI

SEPTEMBER 23, 2024

Synergy Between Artificial Intelligence and Data Science AI and Data Science complement each other through their unique but interconnected roles in data processing and analysis. Deep Learning: Advanced neural networks drive Deep Learning , allowing AI to process vast amounts of data and recognise complex patterns.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Data Science Machine Learning 52

Data Science Blog

FEBRUARY 28, 2023

Clustering, unterzogen, bei dem die Website-Besucher:innen aufgrund ihrer Ähnlichkeiten in verschiedenen Eigenschaften in Gruppen („Cluster“) eingeteilt wurden. Dieses Clustering lieferte dem Unternehmen bereits wertvolle Informationen. Informationen zu Gerät, Standort, Browser und Betriebssystem waren ebenfalls verfügbar.

Data Science 264

Data Science Clustering Analytics Analytics 264

Mlearning.ai

MAY 23, 2023

Once the data is acquired, it is maintained by performing data cleaning, data warehousing, data staging, and data architecture. Data processing does the task of exploring the data, mining it, and analyzing it which can be finally used to generate the summary of the insights extracted from the data.

Data Science 52

Data Science Decision Trees Machine Learning Machine Learning 52

Data Science Blog

MARCH 30, 2023

Diese Anwendungsfälle sind jedoch analytisch recht trivial und bereits mit einfacher BI (Business Intelligence) oder dedizierten Analysen ganz ohne Process Mining bereits viel schneller aufzuspüren. Wie anfangs erwähnt, haben Unternehmen bei der Einführung von Process Mining die Qual der Wahl.

Data Warehouse 147

Data Warehouse Business Intelligence Business Intelligence Power BI 147

Pickl AI

JANUARY 19, 2025

Summary: Data warehousing and data mining are crucial for effective data management. Data warehousing focuses on storing and organizing data for easy access, while data mining extracts valuable insights from that data. It ensures data quality, consistency, and accessibility over time.

Data Mining 52

Data Mining Data Mining Data Mining Data Warehouse 52