Remove Data Analysis Remove Data Mining Remove ETL
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A Beginner’s Guide to Data Warehousing

Unite.AI

They can contain structured, unstructured, or semi-structured data. These can include structured databases, log files, CSV files, transaction tables, third-party business tools, sensor data, etc. Improved Decision Making: A data warehouse supports BI functions like data mining, visualization, and reporting.

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A beginner tale of Data Science

Becoming Human

- a beginner question Let’s start with the basic thing if I talk about the formal definition of Data Science so it’s like “Data science encompasses preparing data for analysis, including cleansing, aggregating, and manipulating the data to perform advanced data analysis” , is the definition enough explanation of data science?

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A brief history of Data Engineering: From IDS to Real-Time streaming

Artificial Corner

The development of data warehouses marked a shift in how businesses used data, moving from transactional processing to data analysis and decision support. OLAP, a term coined by Dr. Edgar Codd, the father of the relational database, is a technology that allows users to analyze data from multiple dimensions.

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Top Data Analytics Skills and Platforms for 2023

ODSC - Open Data Science

We looked at over 25,000 job descriptions, and these are the data analytics platforms, tools, and skills that employers are looking for in 2023. Excel is the second most sought-after tool in our chart as you’ll see below as it’s still an industry standard for data management and analytics.

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Top Predictive Analytics Tools/Platforms (2023)

Marktechpost

Predictive analytics uses methods from data mining, statistics, machine learning, mathematical modeling, and artificial intelligence to make future predictions about unknowable events. It creates forecasts using historical data. Embed, Infusion Apps, and Analytics are the three platform components used for data analysis.

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Exploring the Power of Data Warehouse Functionality

Pickl AI

Let’s delve into the key components that form the backbone of a data warehouse: Source Systems These are the operational databases, CRM systems, and other applications that generate the raw data feeding the data warehouse. Data Extraction, Transformation, and Loading (ETL) This is the workhorse of architecture.

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