Data Science Blog

AUGUST 22, 2024

Businesses need to understand the trends in data preparation to adapt and succeed. If you input poor-quality data into an AI system, the results will be poor. This principle highlights the need for careful data preparation, ensuring that the input data is accurate, consistent, and relevant.

Data Preparation 130

Data Preparation Data Quality AI AI 130

Pickl AI

NOVEMBER 4, 2024

Summary: The fundamentals of Data Engineering encompass essential practices like data modelling, warehousing, pipelines, and integration. Understanding these concepts enables professionals to build robust systems that facilitate effective data management and insightful analysis. What is Data Engineering?

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

ODSC - Open Data Science

MAY 10, 2023

Data-centric AI, in his opinion, is based on the following principles: It’s time to focus on the data — after all the progress achieved in algorithms means it’s now time to spend more time on the data Inconsistent data labels are common since reasonable, well-trained people can see things differently.

Data Scientist 87

Data Scientist Data Science Deep Learning Deep Learning 87

ODSC - Open Data Science

NOVEMBER 19, 2024

ODSC West 2024 showcased a wide range of talks and workshops from leading data science, AI, and machine learning experts. This blog highlights some of the most impactful AI slides from the world’s best data science instructors, focusing on cutting-edge advancements in AI, data modeling, and deployment strategies.

Deep Learning 52

Deep Learning Deep Learning Data Science AI 52

Iguazio

DECEMBER 14, 2023

Who This Book Is For This book is for practitioners in charge of building, managing, maintaining, and operationalizing the ML process end to end: Data science / AI / ML leaders: Heads of Data Science, VPs of Advanced Analytics, AI Lead etc. Exploratory data analysis (EDA) and modeling.

ML 52

ML ML Data Science Data Preparation 52

Tableau

SEPTEMBER 23, 2021

For example, Tableau data engineers want a single source of truth to help avoid creating inconsistencies in data sets, while line-of-business users are concerned with how to access the latest data for trusted analysis when they need it most. Data modeling. Data migration . Data architecture.

Data Governance 102

Data Governance Analytics Analytics Tableau 102

Tableau

SEPTEMBER 23, 2021

For example, Tableau data engineers want a single source of truth to help avoid creating inconsistencies in data sets, while line-of-business users are concerned with how to access the latest data for trusted analysis when they need it most. Data modeling. Data migration . Data architecture.

Data Governance 97

Data Governance Analytics Analytics Tableau 97