Remove Data Analysis Remove Linked Data Remove ML
article thumbnail

This AI Paper Introduces A Comprehensive RDF Dataset With Over 26 Billion Triples Covering Scholarly Data Across All Scientific Disciplines

Marktechpost

Modeling the underlying academic data as an RDF knowledge graph (KG) is one efficient method. This makes standardization, visualization, and interlinking with Linked Data resources easier. As a result, scholarly KGs are essential for converting document-centric academic material into linked and automatable knowledge structures.

article thumbnail

Present and future of data cubes: an European EO perspective

Mlearning.ai

In the most generic terms, every project starts with raw data, which comes from observations and measurements i.e. it is directly downloaded from instruments. It can be gradually “enriched” so the typical hierarchy of data is thus: Raw data ↓ Cleaned dataAnalysis-ready data ↓ Decision-ready data ↓ Decisions.

professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Build a Stocks Price Prediction App powered by Snowflake, AWS, Python and Streamlit?—?Part 2 of 3

Mlearning.ai

Data storage : Store the data in a Snowflake data warehouse by creating a data pipe between AWS and Snowflake. Data Extraction, Preprocessing & EDA : Extract & Pre-process the data using Python and perform basic Exploratory Data Analysis. Please refer to this documentation link.

Python 52
article thumbnail

[Updated] 100+ Top Data Science Interview Questions

Mlearning.ai

In this article, we will explore some common data science interview questions that will help you prepare and increase your chances of success. Read the full blog here —  [link] Data Science Interview Questions for Freshers 1. What is Data Science? This model also learns noise from the data set that is meant for training.

article thumbnail

Future-Proof Your Company’s AI Strategy: How a Strong Data Foundation Can Set You Up for Sustainable Innovation

Unite.AI

After all, companies cant have AI development without fixing data first, and leaders are pulling away from the pack by using their more matured capabilities to better ideate, prioritize, and ensure adoption of more differentiating and transformational uses of data and AI.