Knowledge Hut

JANUARY 25, 2024

What is Data Cleaning? Data cleaning, also known as data cleansing, is the essential process of identifying and rectifying errors, inaccuracies, inconsistencies, and imperfections in a dataset. It involves removing or correcting incorrect, corrupted, improperly formatted, duplicate, or incomplete data.

Data Cleanse 52

Data Cleanse Datasets Data Preparation Data Science 52

AltexSoft

MAY 12, 2021

Particularly, we’ll present our findings on what it takes to prepare a medical image dataset, which models show best results in medical image recognition , and how to enhance the accuracy of predictions. Otherwise, let’s proceed to the first and most fundamental step in building AI-fueled computer vision tools — data preparation.

Medical 72

Medical Healthcare Datasets Machine Learning 72

ProjectPro

FEBRUARY 8, 2023

Scale Existing Python Code with Ray Python is popular among data scientists and developers because it is user-friendly and offers extensive built-in data processing libraries. For analyzing huge datasets, they want to employ familiar Python primitive types. Then Redshift can be used as a data warehousing tool for this.

AWS 98

AWS Scala Metadata Data Lake 98

ProjectPro

AUGUST 24, 2021

And if you are aspiring to become a data engineer, you must focus on these skills and practice at least one project around each of them to stand out from other candidates. Explore different types of Data Formats: A data engineer works with various dataset formats like.csv,josn,xlx, etc.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

ProjectPro

NOVEMBER 15, 2021

In addition to analytics and data science, RAPIDS focuses on everyday data preparation tasks. Multi-node, multi-GPU deployments are also supported by RAPIDS, allowing for substantially faster processing and training on much bigger datasets. It offers a fault-tolerant storage engine that prioritizes data security.

Big Data 52

Big Data Project Metadata Programming Language 52

Rockset

FEBRUARY 25, 2021

Instead, this data is often semi-structured in JSON or arrays. Often this lack of structure forces developers to spend a lot of their time engineering ETL and data pipelines so that analysts can access the complex datasets. From there, you can join and aggregate data without using complex code.

SQL 40

SQL Data Pipeline Kafka Database 40