The Unofficial Google Data Science Blog

JULY 18, 2023

E ven after we account for disagreement, human ratings may not measure exactly what we want to measure. Researchers and practitioners have been using human-labeled data for many years, trying to understand all sorts of abstract concepts that we could not measure otherwise. That’s the focus of this blog post.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

CIO Business Intelligence

JULY 5, 2022

Business analytics is the practical application of statistical analysis and technologies on business data to identify and anticipate trends and predict business outcomes. Business analytics also involves data mining, statistical analysis, predictive modeling, and the like, but is focused on driving better business decisions.

Business Analytics 127

Business Analytics Prescriptive Analytics Data mining Diagnostic Analytics 127

DataKitchen

MAY 10, 2021

Some will argue that observability is nothing more than testing and monitoring applications using tests, metrics, logs, and other artifacts. Below we will explain how to virtually eliminate data errors using DataOps automation and the simple building blocks of data and analytics testing and monitoring. . Tie tests to alerts.

Testing 214

Testing Manufacturing Data Quality Statistics 214

CIO Business Intelligence

JUNE 7, 2022

The chief aim of data analytics is to apply statistical analysis and technologies on data to find trends and solve problems. Data analytics draws from a range of disciplines — including computer programming, mathematics, and statistics — to perform analysis on data in an effort to describe, predict, and improve performance.

Data Analytics 129

Data Analytics Diagnostic Analytics Management Analytics 129

IBM Big Data Hub

APRIL 9, 2024

.” Watsonx.data uses machine learning (ML) applications to simulate data that represents ball positioning projections. We also measure response time since this is a near real-time system. ” Training and testing models The Masters digital team used watsonx.ai

Management 79

Management IT Machine Learning Metrics 79

CIO Business Intelligence

JUNE 14, 2023

Certifications measure your knowledge and skills against industry- and vendor-specific benchmarks to prove to employers that you have the right skillset. Organization: AWS Price: US$300 How to prepare: Amazon offers free exam guides, sample questions, practice tests, and digital training.

Big Data 124

Big Data Data Analytics Analytics Predictive Modeling 124

IBM Big Data Hub

APRIL 13, 2023

After developing a machine learning model, you need a place to run your model and serve predictions. Someone with the knowledge of SQL and access to a Db2 instance, where the in-database ML feature is enabled, can easily learn to build and use a machine learning model in the database. Create a TRAIN partition.

Modeling 82

Modeling Statistics Machine Learning Testing 82

IBM Big Data Hub

NOVEMBER 29, 2023

With the emergence of new advances and applications in machine learning models and artificial intelligence, including generative AI, generative adversarial networks, computer vision and transformers, many businesses are seeking to address their most pressing real-world data challenges using both types of synthetic data: structured and unstructured.

Metrics 81

Metrics Machine Learning Statistics Risk 81

O'Reilly on Data

MARCH 31, 2020

If you’re already a software product manager (PM), you have a head start on becoming a PM for artificial intelligence (AI) or machine learning (ML). AI products are automated systems that collect and learn from data to make user-facing decisions. We won’t go into the mathematics or engineering of modern machine learning here.

Management 333

Management Machine Learning Experimentation Metrics 333

Business Over Broadway

SEPTEMBER 27, 2018

In this paper, I show you how marketers can improve their customer retention efforts by 1) integrating disparate data silos and 2) employing machine learning predictive analytics. Your marketing strategy is only as good as your ability to deliver measurable results. genetic counseling, genetic testing).

Machine Learning 76

Machine Learning Data Integration Marketing Predictive Modeling 76

O'Reilly on Data

JULY 28, 2020

Product Managers are responsible for the successful development, testing, release, and adoption of a product, and for leading the team that implements those milestones. It’s often difficult for businesses without a mature data or machine learning practice to define and agree on metrics. Agreeing on metrics.

Marketing 362

Marketing Experimentation Metrics Testing 362

CIO Business Intelligence

JULY 24, 2023

The country’s premier football division, LaLiga, is leveraging artificial intelligence and machine learning (ML) to deliver new insights to players and coaches, and to transform how fans enjoy and understand the game. We had some tests in the laboratory first, and then we tested with the fans.

Broadcasting 85

Broadcasting Recreation/Entertainment Testing Machine Learning 85

datapine

JANUARY 6, 2022

Yet, before any serious data interpretation inquiry can begin, it should be understood that visual presentations of data findings are irrelevant unless a sound decision is made regarding scales of measurement. Interval: a measurement scale where data is grouped into categories with orderly and equal distances between the categories.

Visualization 246

Visualization Dashboards Cost-Benefit Measurement 246

IBM Big Data Hub

AUGUST 9, 2023

A phishing simulation is a cybersecurity exercise that tests an organization’s ability to recognize and respond to a phishing attack. Why phishing simulations are important Recent statistics show phishing threats continue to rise. The only difference is that recipients who take the bait (e.g., million phishing sites.

Testing 74

Testing Measurement Behavioral Analytics Reporting 74

Smart Data Collective

SEPTEMBER 19, 2022

Statistics show that 93% of customers will offer repeat business when they encounter a positive customer experience. However, fintech businesses can use big data and machine learning to build fraud detection systems that uncover anomalies in real time. Better UI/UX based on A/B testing. Improving Security.

Big Data 129

Big Data ROI Measurement Risk 129

CIO Business Intelligence

NOVEMBER 7, 2023

As the end of 2023 approaches, it becomes imperative to assess the current landscape of cybersecurity threats, explore potential strategies to combat them, and explore the new practice measures that can be taken. A BCP typically includes four steps: business impact analysis, recovery strategies, plan development, and testing and exercises.

Measurement 119

Measurement Risk Strategy Statistics 119

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., Taking measurements at parameter settings further from control parameter settings leads to a lower variance estimate of the slope of the line relating the metric to the parameter.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

Smarten

DECEMBER 3, 2023

‘Augmented analytics is the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation and insight explanation to augment how people explore and analyze data in analytics and BI platforms. What is self-service analytics? We should probably explain before we move on.

Key Performance Indicator 52

Key Performance Indicator Analytics IT Predictive Analytics 52

AWS Big Data

MARCH 27, 2024

By testing the solution against key metrics, a POC provides insights that allow you to make an informed decision on the suitability of the technology for the intended use case. Complete the implementation tasks such as data ingestion and performance testing. Collect data metrics and statistics on the completed tasks.

Testing 99

Testing Data Warehouse Metrics Cost-Benefit 99

O'Reilly on Data

MAY 18, 2020

And last is the probabilistic nature of statistics and machine learning (ML). Most AI models decay overtime: This phenomenon, known more widely as model decay , refers to the declining quality of AI system results over time, as patterns in new data drift away from patterns learned in training data.

Risk 359

Risk Modeling Data Processing Software 359

DataRobot Blog

MAY 26, 2022

Validating Modern Machine Learning (ML) Methods Prior to Productionization. Validating Machine Learning Models. This may be accomplished through a wide variety of tests, to develop a deeper introspection into how the model behaves.

Risk 52

Risk Modeling Metrics Business Objectives 52

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. What is MLOps?

Data Science 53

Data Science Machine Learning Cost-Benefit Deep Learning 53

O'Reilly on Data

JULY 15, 2019

As companies use machine learning (ML) and AI technologies across a broader suite of products and services, it’s clear that new tools, best practices, and new organizational structures will be needed. What cultural and organizational changes will be needed to accommodate the rise of machine and learning and AI?

Machine Learning 37

Machine Learning Management Enterprise Risk Management 37

Smarten

JUNE 17, 2022

After completion of the testing procedure, the certificate is provided to show that all requirements were met. As a certified CERT-IN service and product provider, Smarten adds additional security assurances to its already rich foundation of security measures and methodologies to support clients, partners and stakeholders.

Analytics 67

Analytics IT Business Intelligence Predictive Modeling 67

Cloudera

DECEMBER 3, 2021

This involves identifying, quantifying and being able to measure ethical considerations while balancing these with performance objectives. Uncertainty is a measure of our confidence in the predictions made by a system. Which measures of performance we choose depends upon the nature of the problem. System Design. Model Drift.

Uncertainty 104

Uncertainty Measurement Metrics Risk 104

Domino Data Lab

JULY 1, 2021

statistical model-based techniques – Using Machine Learning we can streamline and simplify the process of building NER models, because this approach does not need a predefined exhaustive set of naming rules. The process of statistical learning can automatically extract said rules from a training dataset.

Modeling 111

Modeling Statistics Testing Metrics 111

FineReport

DECEMBER 9, 2021

Business intelligence strategy is seen as a roadmap designed to help companies measure their performance and strengthen their performance through architecture and solutions. KPI (Key Performance Indicator)-the indicator you will use to measure performance. How to develop a smart BI strategy? Untracked metrics: data you will not track.

Strategy 98

Strategy Business Intelligence KPI Key Performance Indicator 98

Domino Data Lab

MAY 8, 2019

I’ve been out themespotting and this month’s article features several emerging threads adjacent to the interpretability of machine learning models. Machine learning model interpretability. Other good related papers include: “ Towards A Rigorous Science of Interpretable Machine Learning ”. Not yet, if ever.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

JUNE 23, 2019

Pete Skomoroch ’s “ Product Management for AI ”session at Rev provided a “crash course” on what product managers and leaders need to know about shipping machine learning (ML) projects and how to navigate key challenges. Be aware that machine learning often involves working on something that isn’t guaranteed to work.

Management 110

Management Machine Learning Experimentation Metrics 110

Domino Data Lab

OCTOBER 2, 2019

This article covers causal relationships and includes a chapter excerpt from the book Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications by Andrew Kelleher and Adam Kelleher. The case that might be familiar to you is an AB test. A complementary Domino project is available. .

Machine Learning 68

Machine Learning Measurement Modeling Testing 68

AWS Big Data

MARCH 11, 2024

However, it’s crucial to recognize that this reduction could adversely affect performance if not thoroughly assessed and tested. However, although these indicators can suggest potential overprovisioning, it’s essential to conduct performance testing and validate your scaling patterns before making any adjustments to the number of KPUs.

Management 96

Management Snapshot Metrics Cost-Benefit 96

Sisense

OCTOBER 6, 2020

Most commonly, we think of data as numbers that show information such as sales figures, marketing data, payroll totals, financial statistics, and other data that can be counted and measured objectively. All descriptive statistics can be calculated using quantitative data. Digging into quantitative data. or “how often?”

Statistics 98

Statistics Unstructured Data Data-driven Visualization 98

AWS Big Data

JUNE 15, 2023

QuickSight is a unified BI service providing modern interactive dashboards, natural language querying, paginated reports, machine learning (ML) insights, and embedded analytics at scale. Measures of this type can produce misleading or statistically inaccurate results when added with one another.

Sales 68

Sales Dashboards Visualization Testing 68

DataRobot

APRIL 20, 2021

The performance of any machine learning model is tightly linked to the data it was trained on and validated against. This includes the basics: Computing summary statistics on each feature Measuring associations between features Observing feature distributions and their correlation with the predictive target Identifying outliers.

Measurement 52

Measurement Visualization Advertising Metrics 52

Jen Stirrup

JUNE 16, 2020

Convincing Your HiPPo: Business Statistics for AI and Data Science ( Wednesday 15th July at 12.30pm EST, 5.30pm BST ). To be successful and confident for working AI and Data Science, it’s important to have a grasp of statistics. Join this session to get started with Machine Learning from the Business Intelligence perspective.

Statistics 65

Statistics Visualization Machine Learning Business Intelligence 65

Domino Data Lab

JULY 1, 2020

It also has some neat features around showing you exact spots in your code where violations have been detected, plus it can compute statistics on different violation types. Makes the code easier to test. Not every data scientist feels confident about code testing. Integration tests that target integration of components.

Testing 116

Testing Data Science Machine Learning Data-driven 116

DataRobot

JULY 6, 2021

The Behavioural Insights Team ran an experiment, testing four variations to the language used in reminder letters on a trial group. Use advanced machine learning algorithms to deeply understand customer purchasing behavior. Only 33% of those who receive the reminders respond by paying their taxes.

Marketing 105

Marketing Sales Data-driven Statistics 105