IBM Big Data Hub

JULY 6, 2023

Other challenges include communicating results to non-technical stakeholders, ensuring data security, enabling efficient collaboration between data scientists and data engineers, and determining appropriate key performance indicator (KPI) metrics.

Machine Learning 59

Machine Learning Data Science Statistics Deep Learning 59

Smart Data Collective

FEBRUARY 14, 2021

Do Your Research with Data Mining. This is one of the most important reasons that big data is important in SaaS. There are a lot of metrics that need to be tracked with data analytics tools. Make Sure Your Product Works with AI Testing. AI makes it easier to simulate your products. Contact Other Companies.

Data-driven 86

Data-driven Big Data Deep Learning Software 86

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. Five Reasons And Awesome Testing Ideas. How to focus?"

KPI 124

KPI Metrics Measurement ROI 124

IBM Big Data Hub

AUGUST 11, 2023

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. These insights can help drive decisions in business, and advance the design and testing of applications.

Data Science 57

Data Science Machine Learning Cost-Benefit Deep Learning 57

The Unofficial Google Data Science Blog

OCTOBER 7, 2015

A/B testing is used widely in information technology companies to guide product development and improvements. For questions as disparate as website design and UI, prediction algorithms, or user flows within apps, live traffic tests help developers understand what works well for users and the business, and what doesn’t.

Modeling 61

Modeling Experimentation Knowledge Discovery Testing 61

The Unofficial Google Data Science Blog

JANUARY 14, 2016

The LSOS may do this by exposing a random group of users to the new design and compare them to a control group, and then analyze the effect on important user engagement metrics, such as bounce rate, time to first action, or number of experiences deemed positive. In addition to a suitable metric, we must also choose our experimental unit.

Experimentation 41

Experimentation Statistics Metrics Measurement 41

Occam's Razor

NOVEMBER 13, 2009

Chapter 3 The Awesome World of Clickstream Analysis: Metrics. The second half shows exactly how to pick the best metrics for your org and, my absolute favorite (Page 64), how to diagnose the root cause of a metrics performance. Chapter 7 Failing Faster: Unleashing the Power of Testing and Experimentation. A good thing.

Analytics 71

Analytics Metrics Measurement Testing 71

DataKitchen

OCTOBER 11, 2021

The data engineer then emails the BI Team, who refreshes a Tableau dashboard. Figure 1: Example data pipeline with manual processes. There are no automated tests , so errors frequently pass through the pipeline. The pipeline has automated tests at each step, making sure that each step completes successfully.

Testing 157

Testing Metadata Dashboards Statistics 157

Occam's Razor

SEPTEMBER 15, 2014

They will need two different implementations, it is quite likely that you will end up with two sets of metrics (more people focused for mobile apps, more visit focused for sites). Mobile content consumption, behavior along key metrics (time, bounces etc.) If you have ecommerce you will see key metrics related to money making.

Metrics 141

Metrics Reporting Analytics Marketing 141

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

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

Domino Data Lab

MAY 20, 2021

Further, imbalanced data exacerbates problems arising from the curse of dimensionality often found in such biological data. def get_neigbours(M, k): nn = NearestNeighbors(n_neighbors=k+1, metric="euclidean").fit(M) Their tests are performed using C4.5-generated 1988), E-state data (Hall et al., Quinlan, J.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

datapine

JULY 24, 2019

Data has never been more readily accessible. Approaches to communication are changing, and success in today’s technology-driven world correlates directly to the quantity rather than the quality of one’s information—metrics relating to the business, the client, the competitor, and the market. 3) Design data to avoid clutter.

Dashboards 148

Dashboards Business Intelligence Visualization KPI 148

Domino Data Lab

AUGUST 1, 2021

Because of its architecture, intrinsically explainable ANNs can be optimised not just on its prediction performance, but also on its explainability metric. After forming the X and y variables, we split the data into training and test sets. For sample 23 from the test set, the model is leaning towards a bad credit prediction.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

datapine

NOVEMBER 27, 2019

Another increasing factor in the future of business intelligence is testing AI in a duel. Predictive analytics is the practice of extracting information from existing data sets in order to forecast future probabilities. It’s an extension of data mining which refers only to past data.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

Alation

AUGUST 26, 2021

Today, BI represents a $23 billion market and umbrella term that describes a system for data-driven decision-making. BI leverages and synthesizes data from analytics, data mining, and visualization tools to deliver quick snapshots of business health to key stakeholders, and empower those people to make better choices.

Metadata 64

Metadata Data Governance Dashboards Software 64

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