IBM Big Data Hub

SEPTEMBER 19, 2023

The data science lifecycle Data science is iterative, meaning data scientists form hypotheses and experiment to see if a desired outcome can be achieved using available data. The dedicated data analyst Virtually any stakeholder of any discipline can analyze data.

Data Science 76

Data Science Data Analytics Prescriptive Analytics Analytics 76

Sisense

APRIL 26, 2020

Cloud data warehouses: The new era of data storage. Cloud data warehouses aggregate data from different sources into a central, consistent data store to support various business, analytics, visualization, AI, and ML purposes. Making life better for data professionals.

Unstructured Data 52

Unstructured Data Data Warehouse Structured Data Data mining 52

FineReport

MARCH 24, 2024

Data analysts interpret data using statistical techniques, develop databases and data collection systems, and identify process improvement opportunities. They should possess technical expertise in data models, database design, and data mining, along with proficiency in reporting packages, databases, and programming languages.

Statistics 52

Statistics Data mining Visualization Reporting 52

Peter James Thomas

AUGUST 18, 2018

That was the Science, here comes the Technology… A Brief Hydrology of Data Lakes. Even back then, these were used for activities such as Analytics , Dashboards , Statistical Modelling , Data Mining and Advanced Visualisation. This is the essence of Convergent Evolution.

Data Lake 63

Data Lake Data Warehouse Data mining Statistics 63

datapine

AUGUST 29, 2022

An excerpt from a rave review : “I would definitely recommend this book to everyone interested in learning about data from scratch and would say it is the finest resource available among all other Big Data Analytics books.”. 6) Lean Analytics: Use Data to Build a Better Startup Faster, by Alistair Croll and Benjamin Yoskovitz.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

Jet Global

MAY 9, 2024

The key components of a data pipeline are typically: Data Sources : The origin of the data, such as a relational database , data warehouse, data lake , file, API, or other data store. This can include tasks such as data ingestion, cleansing, filtering, aggregation, or standardization.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52