ODSC - Open Data Science

MAY 10, 2023

Unfortunately, even the data science industry — which should recognize tabular data’s true value — often underestimates its relevance in AI. Many mistakenly equate tabular data with business intelligence rather than AI, leading to a dismissive attitude toward its sophistication.

Data Scientist 87

Data Scientist Data Science Deep Learning Deep Learning 87

IBM Journey to AI blog

SEPTEMBER 19, 2023

Data analytics is a task that resides under the data science umbrella and is done to query, interpret and visualize datasets. Data scientists will often perform data analysis tasks to understand a dataset or evaluate outcomes.

Data Science 94

Data Science Analytics Analytics Data Scientist 94

IBM Journey to AI blog

JANUARY 5, 2023

The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases. Practice proper data hygiene across interfaces.

Data Quality 52

Data Quality Data Lakes Data Warehouse Big Data 52

Mlearning.ai

FEBRUARY 16, 2023

Today, companies are facing a continual need to store tremendous volumes of data. The demand for information repositories enabling business intelligence and analytics is growing exponentially, giving birth to cloud solutions. Data warehousing is a vital constituent of any business intelligence operation.

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 52

Data Science Blog

AUGUST 22, 2024

The effectiveness of generative AI is linked to the data it uses. Similar to how a chef needs fresh ingredients to prepare a meal, generative AI needs well-prepared, clean data to produce outputs. Businesses need to understand the trends in data preparation to adapt and succeed.

Data Preparation 130

Data Preparation Data Quality AI AI 130

IBM Journey to AI blog

OCTOBER 16, 2023

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. It also lets you choose the right engine for the right workload at the right cost, potentially reducing your data warehouse costs by optimizing workloads.

AI 88

AI AI Data Warehouse ML 88

Pickl AI

JULY 25, 2024

Data Warehouse: Its significance and relevance in the data world. Exploring Differences: Database vs Data Warehouse. Role in Data Warehousing and Business Intelligence Dimensional modelling plays a crucial role in data warehousing and business intelligence by structuring data to enhance performance and usability.

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 52

phData

AUGUST 10, 2023

The implementation of a data vault architecture requires the integration of multiple technologies to effectively support the design principles and meet the organization’s requirements. Data Vault Automation Working at scale can be challenging, especially when managing the data model.

SQL 52

SQL Data Observability Data Quality Data Pipeline 52

Alation

JULY 19, 2022

The traditional data science workflow , as defined by Joe Blitzstein and Hanspeter Pfister of Harvard University, contains 5 key steps: Ask a question. Get the data. Explore the data. Model the data. A data catalog can assist directly with every step, but model development.

Data Scientist 59

Data Scientist Data Quality Data Science Data Analyst 59

phData

MAY 11, 2023

One scenario could be multiple team members who will each work on ingesting and processing data from one of the source systems. Figure 3: Source Systems made into Modules Data Modeling The process to prepare data for consumption by the data visualization layer follows a highly-repeatable pattern.

ETL 52

ETL Analytics Analytics Data Models 52

Alation

SEPTEMBER 7, 2021

Alation TrustCheck provides quality flags that signal endorsement, warning, or deprecation; this gives you instant understanding of quality and helps you trust data. Data quality details signal to users whether data can be trusted or used. This enables stewards to create policies and get feeds in intelligent cues in real-time.

Data Governance 59

Data Governance Data Scientist Data Quality Data Profiling 59

Pickl AI

JANUARY 16, 2024

Predictive Modeler Harnessing the power of algorithms to forecast future trends, aiding businesses in strategic decision-making.

Data Analyst 52

Data Analyst Data Analysis Data Analysis Analytics 52

Pickl AI

DECEMBER 4, 2023

Python, R), Machine Learning, statistical modelling, Data Visualisation Pursue advanced degrees, participate in hackathons, and build a strong online presence by sharing projects on platforms like GitHub. 9,43,649 Business acumen, Data Visualisation tools (e.g., . ₹ 12,00000 Programming (e.g.,

Data Analysis 52

Data Analysis Data Analysis Artificial Intelligence Artificial Intelligence 52

Data Science Blog

JULY 23, 2023

Companies use Business Intelligence (BI), Data Science , and Process Mining to leverage data for better decision-making, improve operational efficiency, and gain a competitive edge. Data Mesh on Azure Cloud with Databricks and Delta Lake for Applications of Business Intelligence, Data Science and Process Mining.

Data Science 246

Data Science Azure Power BI Business Intelligence 246

Tableau

DECEMBER 1, 2021

Innovation is necessary to use data effectively in the pursuit of a better world, particularly because data continues to increase in size and richness. April 2018), which focused on users who do understand joins and curating federated data sources. Another key data computation moment was Hyper in v10.5 (Jan

Tableau 145

Tableau ML ML Database 145

Tableau

DECEMBER 1, 2021

Innovation is necessary to use data effectively in the pursuit of a better world, particularly because data continues to increase in size and richness. April 2018), which focused on users who do understand joins and curating federated data sources. Another key data computation moment was Hyper in v10.5 (Jan

Tableau 98

Tableau ML ML Database 98

phData

JULY 18, 2023

CDWs are designed for running large and complex queries across vast amounts of data, making them ideal for centralizing an organization’s analytical data for the purpose of business intelligence and data analytics applications. It should also enable easy sharing of insights across the organization.

Data Warehouse 52

Data Warehouse Analytics Analytics Cloud Data 52

Pickl AI

MARCH 19, 2025

Summary: Data engineering tools streamline data collection, storage, and processing. Tools like Python, SQL, Apache Spark, and Snowflake help engineers automate workflows and improve efficiency. Learning these tools is crucial for building scalable data pipelines. Thats where data engineering tools come in!

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52