datapine

AUGUST 14, 2019

Data science has become an extremely rewarding career choice for people interested in extracting, manipulating, and generating insights out of large volumes of data. To fully leverage the power of data science, scientists often need to obtain skills in databases, statistical programming tools, and data visualizations.

Data Science 214

Data Science Statistics Business Intelligence Visualization 214

FineReport

MARCH 24, 2024

Descriptive analytics: Assessing historical trends, such as sales and revenue. Predictive analytics: Forecasting likely outcomes based on patterns and trends to facilitate proactive decision-making. Proficiency in programming languages such as R and SAS is essential for data gathering, cleaning, and visualization.

Statistics 52

Statistics Data mining Visualization Reporting 52

Jet Global

JULY 11, 2019

With major advances being made in artificial intelligence and machine learning, businesses are investing heavily in advanced analytics to get ahead of the competition and increase their bottom line. We’ll explain what it is, how it works, and ways to start using demand forecasting with business intelligence software.

Business Intelligence 72

Business Intelligence Forecasting Data Warehouse Predictive Analytics 72

Jet Global

OCTOBER 1, 2019

With major advances being made in artificial intelligence and machine learning, businesses are investing heavily in advanced analytics to get ahead of the competition and increase their bottom line. We’ll explain what it is, how it works, and ways to start using demand forecasting with business intelligence software.

Business Intelligence 40

Business Intelligence Forecasting Data Warehouse Predictive Analytics 40

Grooper

AUGUST 19, 2019

Applied analytics Business analytics Machine learning and data science. Applied Analytics. Applied analytics is all about building a business analytics portfolio of actionable insights which directly affect and improve business processes. Data pipelines. Business Analytics.

Analytics 54

Analytics Machine Learning Data Science Data-driven 54

Smarten

SEPTEMBER 15, 2017

Don’t become a failure statistic! Requirements Planning for Data Analytics. Many organizations are so anxious to get into analytics that they fail to consider the depth and breadth of their needs. What kind of statistical data, report capability and security will you need? How will you manage growth?

Analytics 40

Analytics Statistics Data Governance Cost-Benefit 40

Domino Data Lab

JULY 22, 2019

He was saying this doesn’t belong just in statistics. He also really informed a lot of the early thinking about data visualization. It involved a lot of interesting work on something new that was data management. To some extent, academia still struggles a lot with how to stick data science into some sort of discipline.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109