Knowledge Hut

NOVEMBER 3, 2023

Data Visualization It provides a wide range of networks, diagrams, and maps. Boasts an extensive library of customizable visuals for diverse data representation. Augmented Analytics Incorporates machine learning and AI for automated data preparation, insights, and suggestions.

BI 52

BI Cloud Retail Medical 52

AltexSoft

MAY 12, 2022

Particularly, we’ll explain how to obtain audio data, prepare it for analysis, and choose the right ML model to achieve the highest prediction accuracy. But first, let’s go over the basics: What is the audio analysis, and what makes audio data so challenging to deal with. Audio data transformation basics to know.

Machine Learning 122

Machine Learning Building Datasets Healthcare 122

Knowledge Hut

JANUARY 30, 2024

Audio Transcription: Audio data can be transcribed into text using speech recognition technology, enabling the extraction of spoken content for analysis, such as customer service call logs or voice recordings. Extraction: This initial step involves retrieving data from one or multiple sources or systems.

ETL Tools 52

ETL Tools Database-centric Data Mining Data Cleanse 52

Monte Carlo

MAY 25, 2023

Some of the value companies can generate from data orchestration tools include: Faster time-to-insights. Automated data orchestration removes data bottlenecks by eliminating the need for manual data preparation, enabling analysts to both extract and activate data in real-time. Improved data governance.

Data Pipeline 52

Data Pipeline Data Workflow Data Data Governance 52

Ascend.io

JANUARY 2, 2024

The transformation components can involve a wide array of operations such as data augmentation, filtering, grouping, aggregation, standardization, sorting, deduplication, validation, and verification. The goal is to cleanse, merge, and optimize the data, preparing it for insightful analysis and informed decision-making.

Data Pipeline 52

Data Pipeline Building Raw Data Data Warehouse 52

Knowledge Hut

JUNE 20, 2023

Additionally, they create and test the systems necessary to gather and process data for predictive modelling. Data engineers play three important roles: Generalist: With a key focus, data engineers often serve in small teams to complete end-to-end data collection, intake, and processing.

Machine Learning 52

Machine Learning Data Engineering Data Engineer Engineering 52

ProjectPro

JANUARY 31, 2023

There are three steps involved in the deployment of a big data model: Data Ingestion: This is the first step in deploying a big data model - Data ingestion, i.e., extracting data from multiple data sources. It ensures that the data collected from cloud sources or local databases is complete and accurate.

Big Data 52

Big Data Hadoop AWS Relational Database 52