Smart Data Collective

OCTOBER 14, 2019

That, along with data mining can help if the developer wants to work with supply chains, for example. They can use predictive, descriptive and prescriptive analytics to help CSCOs turn metrics into insights for better decision-making. Statistics, qualitative analysis and quant are some of the backbones of big data.

Big Data 75

Big Data Software Data mining Prescriptive Analytics 75

BizAcuity

APRIL 28, 2019

By clubbing various techniques like data mining, machine learning, artificial intelligence and statistical modelling, it makes predictions about events in the future. Also, we noticed that, earlier, the data consisted of only a few metrics, based on which the team classified its customers.

Predictive Analytics 98

Predictive Analytics Analytics Big Data Data mining 98

Sisense

APRIL 10, 2020

R is a tool built by statisticians mainly for mathematics, statistics, research, and data analysis. These visualizations are useful for helping people visualize and understand trends , outliers, and patterns in data. These libraries are used for data collection, analysis, data mining, visualizations, and ML modeling.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

Sisense

AUGUST 11, 2019

BA is a catch-all expression for approaches and technologies you can use to access and explore your company’s data, with a view to drawing out new, useful insights to improve business planning and boost future performance. What About “Business Intelligence”? But on the whole, BI is more concerned with the whats and the hows than the whys.

Business Intelligence 75

Business Intelligence Business Analytics Analytics OLAP 75

The Unofficial Google Data Science Blog

JANUARY 14, 2016

Unlike experimentation in some other areas, LSOS experiments present a surprising challenge to statisticians — even though we operate in the realm of “big data”, the statistical uncertainty in our experiments can be substantial. We must therefore maintain statistical rigor in quantifying experimental uncertainty.

Experimentation 41

Experimentation Statistics Metrics Measurement 41

Occam's Razor

AUGUST 22, 2011

" ~ Web Metrics: "What is a KPI? " + Standard Metrics Revisited Series. "Engagement" Is Not A Metric, It's An Excuse. Data Mining And Predictive Analytics On Web Data Works? Web Analytics Data Sampling 411. Overview & Importance of Qualitative Metrics.

KPI 124

KPI Metrics Measurement ROI 124

datapine

MAY 14, 2019

This interdisciplinary field of scientific methods, processes, and systems helps people extract knowledge or insights from data in a host of forms, either structured or unstructured, similar to data mining.

Data Science 350

Data Science Machine Learning Data-driven Big Data 350

IBM Big Data Hub

AUGUST 11, 2023

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects.

Data Science 60

Data Science Machine Learning Cost-Benefit Deep Learning 60

The Unofficial Google Data Science Blog

APRIL 23, 2024

the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g., Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

datapine

MARCH 25, 2019

What Is A Data Analysis Method? Data analysis method focuses on strategic approaches to taking raw data, mining for insights that are relevant to the business’s primary goals, and drilling down into this information to transform metrics, facts, and figures into initiatives that benefit improvement.

Key Performance Indicator 217

Key Performance Indicator Statistics Visualization Big Data 217

CIO Business Intelligence

JANUARY 20, 2023

The potential use cases for BI extend beyond the typical business performance metrics of improved sales and reduced costs. BI focuses on descriptive analytics, data collection, data storage, knowledge management, and data analysis to evaluate past business data and better understand currently known information.

Business Intelligence 144

Business Intelligence Dashboards Data mining OLAP 144

datapine

MARCH 25, 2022

There is not a clear line between business intelligence and analytics, but they are extremely connected and interlaced in their approach towards resolving business issues, providing insights on past and present data, and defining future decisions. But let’s see in more detail what experts say and how can we connect and differentiate the both.

Business Intelligence 264

Business Intelligence Analytics Statistics Dashboards 264

Smart Data Collective

OCTOBER 12, 2022

Fortunately, a number of data analytics tools can be very helpful here. You will want to make the most of big data technology if you want to make sensible investment decisions. Statistical Atlas. One major bonus of the Statistical Atlas is that it’s absolutely free to use. Google Street View. But, how can you be sure?

Marketing 124

Marketing Data Analytics Analytics Big Data 124

DataKitchen

OCTOBER 11, 2021

Historic Balance – compares current data to previous or expected values. These tests rely upon historical values as a reference to determine whether data values are reasonable (or within the range of reasonable). . Statistical Process Control – applies statistical methods to control a process.

Testing 157

Testing Metadata Dashboards Statistics 157

datapine

AUGUST 29, 2022

Whether you are a complete novice or a seasoned BI professional, you will find here some books on data analytics that will help you cultivate your understanding of this essential field. Before we delve deeper into the best books for data analytics, here are three big data insights to put their relevance and importance into perspective.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

The Unofficial Google Data Science Blog

FEBRUARY 29, 2016

In this post we explore why some standard statistical techniques to reduce variance are often ineffective in this “data-rich, information-poor” realm. Despite a very large number of experimental units, the experiments conducted by LSOS cannot presume statistical significance of all effects they deem practically significant.

Experimentation 40

Experimentation Metrics Statistics Measurement 40

datapine

NOVEMBER 27, 2019

The demand for real-time online data analysis tools is increasing and the arrival of the IoT (Internet of Things) is also bringing an uncountable amount of data, which will promote the statistical analysis and management at the top of the priorities list. It’s an extension of data mining which refers only to past data.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

Jet Global

MAY 1, 2023

Users Want to Help Themselves Data mining is no longer confined to the research department. Today, every professional has the power to be a “data expert.” As a result, end users can better view shared metrics (backed by accurate data), which ultimately drives performance. Standalone is a thing of the past.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52

Jet Global

MAY 9, 2024

Monitoring can include tracking performance metrics such as execution time and resource usage, and logging errors or failures for troubleshooting and remediation. It also includes data validation and quality checks to ensure the accuracy and integrity of the data being processed.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52