Data Science Dojo

JANUARY 12, 2023

It can process any type of data, regardless of its variety or magnitude, and save it in its original format. Hadoop systems and data lakes are frequently mentioned together. However, instead of using Hadoop, data lakes are increasingly being constructed using cloud object storage services.

Data Lakes 221

Data Lakes Data Warehouse Hadoop Machine Learning 221

Pickl AI

AUGUST 1, 2023

Data Science for Business” by Foster Provost and Tom Fawcett This book bridges the gap between Data Science and business needs. It covers Data Engineering aspects like data preparation, integration, and quality. Ideal for beginners, it illustrates how Data Engineering aligns with business applications.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

Pickl AI

NOVEMBER 4, 2024

By implementing efficient data pipelines , organisations can enhance their data processing capabilities, reduce time spent on data preparation, and improve overall data accessibility. Data Storage Solutions Data storage solutions are critical in determining how data is organised, accessed, and managed.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

Alation

FEBRUARY 20, 2020

Even something like gamification may emerge as a way to fully engage data shoppers as a community. Behind the scenes, ‘backroom services” will power the storefront, performing such tasks as data acquisition, data preparation, data curation and cataloging, and tracking. Building the EDM.

Data Warehouse 52

Data Warehouse Data Lakes Hadoop Data Preparation 52

ODSC - Open Data Science

OCTOBER 7, 2024

Understanding the MLOps Lifecycle The MLOps lifecycle consists of several critical stages, each with its unique challenges: Data Ingestion: Collecting data from various sources and ensuring it’s available for analysis. Data Preparation: Cleaning and transforming raw data to make it usable for machine learning.

Machine Learning 52

Machine Learning Machine Learning AI AI 52

Pickl AI

NOVEMBER 28, 2024

Data Transformation Transforming data prepares it for Machine Learning models. Encoding categorical variables converts non-numeric data into a usable format for ML models, often using techniques like one-hot encoding. Outlier detection identifies extreme values that may skew results and can be removed or adjusted.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Pickl AI

OCTOBER 10, 2024

It integrates well with cloud services, databases, and big data platforms like Hadoop, making it suitable for various data environments. Typical use cases include ETL (Extract, Transform, Load) tasks, data quality enhancement, and data governance across various industries.

Data Quality 52

Data Quality AWS Machine Learning Machine Learning 52