Clean Data, Data Quality and Events

Data Quality Framework: What It Is, Components, and Implementation

DagsHub

AUGUST 23, 2024

As such, the quality of their data can make or break the success of the company. This article will guide you through the concept of a data quality framework, its essential components, and how to implement it effectively within your organization. What is a data quality framework?

Data Quality

Data Quality Data Governance Machine Learning Machine Learning

Elevate Your Data Quality: Unleashing the Power of AI and ML for Scaling Operations

Pickl AI

OCTOBER 18, 2023

How to Scale Your Data Quality Operations with AI and ML: In the fast-paced digital landscape of today, data has become the cornerstone of success for organizations across the globe. Every day, companies generate and collect vast amounts of data, ranging from customer information to market trends.

Data Quality

Data Quality ML ML Machine Learning

The Best Data Management Tools For Small Businesses

Smart Data Collective

APRIL 29, 2020

The extraction of raw data, transforming to a suitable format for business needs, and loading into a data warehouse. Data transformation. This process helps to transform raw data into clean data that can be analysed and aggregated. Data analytics and visualisation. Microsoft Azure.

Data Warehouse

Data Warehouse Azure SQL ETL

Webinars

Agent Tooling: Connecting AI to Your Tools, Systems & Data

Automation, Evolved: Your New Playbook for Smarter Knowledge Work

How to Modernize Manufacturing Without Losing Control

Mastering Apache Airflow® 3.0: What’s New (and What’s Next) for Data Orchestration

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Use mobility data to derive insights using Amazon SageMaker geospatial capabilities

AWS Machine Learning Blog

JANUARY 17, 2024

Geospatial data is data about specific locations on the earth’s surface. It can represent a geographical area as a whole or it can represent an event associated with a geographical area. Analysis of geospatial data is sought after in a few industries. Amazon Macie is used on this S3 bucket to identify and redact and PII.

Clustering

Clustering AWS ML ML

NLP, Tools and Technologies and Career Opportunities

Women in Big Data

DECEMBER 13, 2023

The Bay Area Chapter of Women in Big Data (WiBD) hosted its second successful episode on the NLP (Natural Language Processing), Tools, Technologies and Career opportunities. The event was part of the chapter’s technical talk series 2023. I look forward to attending future events hosted by WiBD”. What is the future of NLP?

Natural Language Processing

Natural Language Processing Big Data Big Data Computer Science

What is The Difference Between Data Analysis and Interpretation?

Pickl AI

FEBRUARY 6, 2025

Overcoming challenges like data quality and bias improves accuracy, helping businesses and researchers make data-driven choices with confidence. Introduction Data Analysis and interpretation are key steps in understanding and making sense of data. Challenges like poor data quality and bias can impact accuracy.

Data Analysis

Data Analysis Data Analysis Data Quality Power BI

Top 5 Challenges faced by Data Scientists

Pickl AI

MARCH 10, 2023

However, despite being a lucrative career option, Data Scientists face several challenges occasionally. The following blog will discuss the familiar Data Science challenges professionals face daily. Furthermore, it ensures that data is consistent while effectively increasing the readability of the data’s algorithm.

Data Scientist

Data Scientist Data Science Apache Hadoop Machine Learning

AI in Time Series Forecasting

Pickl AI

DECEMBER 16, 2024

This step includes: Identifying Data Sources: Determine where data will be sourced from (e.g., Ensuring Time Consistency: Ensure that the data is organized chronologically, as time order is crucial for time series analysis. Cleaning Data: Address any missing values or outliers that could skew results.

AI

AI AI Machine Learning Machine Learning

Debugging data to build better and more fair ML applications

Snorkel AI

APRIL 28, 2023

He presented “Building Machine Learning Systems for the Era of Data-Centric AI” at Snorkel AI’s The Future of Data-Centric AI event in 2022. The talk explored Zhang’s work on how debugging data can lead to more accurate and more fair ML applications. You can then plug in different types of objectives.

ML

ML ML Machine Learning Machine Learning

Debugging data to build better and more fair ML applications

Snorkel AI

APRIL 28, 2023

He presented “Building Machine Learning Systems for the Era of Data-Centric AI” at Snorkel AI’s The Future of Data-Centric AI event in 2022. The talk explored Zhang’s work on how debugging data can lead to more accurate and more fair ML applications. You can then plug in different types of objectives.

ML

ML ML Machine Learning Machine Learning

How to Manage Unstructured Data in AI and Machine Learning Projects

DagsHub

OCTOBER 23, 2024

Now that you know why it is important to manage unstructured data correctly and what problems it can cause, let's examine a typical project workflow for managing unstructured data. They assist in efficiently managing and processing data from multiple sources, ensuring smooth integration and analysis across diverse formats.

Machine Learning

Machine Learning Machine Learning Data Lakes AI

dbt Labs’ Coalesce 2023 Recap

phData

NOVEMBER 13, 2023

Read more about the dbt Explorer: Explore your dbt projects dbt Semantic Layer: Relaunch The dbt Semantic Layer is an innovative approach to solving the common data consistency and trust challenges. These jobs can be triggered via schedule or events, ensuring your data assets are always up-to-date.

Database

Database Business Intelligence Business Intelligence Data Silos

Basic Data Science Terms Every Data Analyst Should Know

Pickl AI

SEPTEMBER 12, 2024

Data Cleaning: Raw data often contains errors, inconsistencies, and missing values. Data cleaning identifies and addresses these issues to ensure data quality and integrity. Data Visualisation: Effective communication of insights is crucial in Data Science.

Data Analyst

Data Analyst Data Science Machine Learning Machine Learning

Large Language Models: A Complete Guide

Heartbeat

MAY 29, 2023

This step involves several tasks, including data cleaning, feature selection, feature engineering, and data normalization. This process ensures that the dataset is of high quality and suitable for machine learning.

Machine Learning

Machine Learning Machine Learning Natural Language Processing Data Preparation

Empowering in Data & Governance: Insights from our WiBD Berlin event

Women in Big Data

JANUARY 25, 2025

Together with the Hertie School , we co-hosted an inspiring event, Empowering in Data & Governance. The event was opened by Aliya Boranbayeva , representing Women in Big Data Berlin and the Hertie School Data Science Lab , alongside Matthew Poet , representing the Hertie School.

Data Governance

Data Governance Big Data Big Data Data Quality

Data Science Current

Data Quality Framework: What It Is, Components, and Implementation

Elevate Your Data Quality: Unleashing the Power of AI and ML for Scaling Operations

Webinars

Trending Sources

The Best Data Management Tools For Small Businesses

Webinars

Use mobility data to derive insights using Amazon SageMaker geospatial capabilities

NLP, Tools and Technologies and Career Opportunities

What is The Difference Between Data Analysis and Interpretation?

Top 5 Challenges faced by Data Scientists

AI in Time Series Forecasting

Debugging data to build better and more fair ML applications

Debugging data to build better and more fair ML applications

How to Manage Unstructured Data in AI and Machine Learning Projects

dbt Labs’ Coalesce 2023 Recap

Basic Data Science Terms Every Data Analyst Should Know

Large Language Models: A Complete Guide

Empowering in Data & Governance: Insights from our WiBD Berlin event

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