Sisense

SEPTEMBER 25, 2020

A data scientist must be skilled in many arts: math and statistics, computer science, and domain knowledge. Statistics and programming go hand in hand. Mastering statistical techniques and knowing how to implement them via a programming language are essential building blocks for advanced analytics. Linear regression.

Statistics 98

Statistics Predictive Modeling Modeling Machine Learning 98

O'Reilly on Data

DECEMBER 12, 2019

Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML. Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1]

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

DataRobot Blog

MAY 26, 2022

Last time , we discussed the steps that a modeler must pay attention to when building out ML models to be utilized within the financial institution. In summary, to ensure that they have built a robust model, modelers must make certain that they have designed the model in a way that is backed by research and industry-adopted practices.

Risk 52

Risk Modeling Metrics Business Objectives 52

CIO Business Intelligence

NOVEMBER 20, 2023

The US Bureau of Labor Statistics has projected that the number of software developers will grow 25% from 2021-31. Well-known metrics, such as deployment frequency, are useful when it comes to tracking teams but not individuals. Then we complemented these with the following four “opportunity-focused metrics.” Talent capability.

Measurement 126

Measurement Metrics Software Testing 126

IBM Big Data Hub

APRIL 9, 2024

Preparing and annotating data IBM watsonx.data helps organizations put their data to work, curating and preparing data for use in AI models and applications. “For the Masters we use 290 traditional AI models to project where golf balls will land,” says Baughman. ” Watsonx.ai ” Watsonx.ai

Management 88

Management IT Machine Learning Metrics 88

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

The Unofficial Google Data Science Blog

DECEMBER 14, 2021

Some of that uncertainty is the result of statistical inference, i.e., using a finite sample of observations for estimation. But there are other kinds of uncertainty, at least as important, that are not statistical in nature. Among these, only statistical uncertainty has formal recognition.

Uncertainty 56

Uncertainty Statistics Measurement Cost-Benefit 56

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 89

Metrics Machine Learning Risk Statistics 89

Domino Data Lab

JULY 1, 2021

In this blog post we present the Named Entity Recognition problem and show how a BiLSTM-CRF model can be fitted using a freely available annotated corpus and Keras. The model achieves relatively high accuracy and all data and code is freely available in the article. How to build a statistical Named Entity Recognition (NER) model.

Modeling 111

Modeling Statistics Testing Metrics 111

DataKitchen

NOVEMBER 18, 2022

As he thinks through the various journeys that data take in his company, Jason sees that his dashboard idea would require extracting or testing for events along the way. Data and tool tests. In addition to the tracking of relationships and quality metrics, DataOps Observability journeys allow users to establish baselines?concrete

Testing 130

Testing Statistics Measurement Metrics 130

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. Data analytics is used across disciplines to find trends and solve problems using data mining , data cleansing, data transformation, data modeling, and more.

Business Analytics 135

Business Analytics Prescriptive Analytics Data mining Diagnostic Analytics 135

Occam's Razor

APRIL 8, 2013

To win in business you need to follow this process: Metrics > Hypothesis > Experiment > Act. We are far too enamored with data collection and reporting the standard metrics we love because others love them because someone else said they were nice so many years ago. This should not be news to you. But it is not routine.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

DataKitchen

OCTOBER 11, 2021

There are no automated tests , so errors frequently pass through the pipeline. There is no process to spin up an isolated dev environment to quickly add a feature, test it with actual data and deploy it to production. The pipeline has automated tests at each step, making sure that each step completes successfully.

Testing 157

Testing Metadata Dashboards Statistics 157

DataKitchen

MAY 10, 2021

Some will argue that observability is nothing more than testing and monitoring applications using tests, metrics, logs, and other artifacts. Since 2008, teams working for our founding team and our customers have delivered 100s of millions of data sets, dashboards, and models with almost no errors. Tie tests to alerts.

Testing 214

Testing Manufacturing Data Quality Statistics 214

AWS Big Data

MARCH 11, 2024

Managed Service for Apache Flink manages the underlying infrastructure and Apache Flink components that provide durable application state, metrics, logs, and more. However, it’s crucial to recognize that this reduction could adversely affect performance if not thoroughly assessed and tested.

Management 96

Management Snapshot Metrics Cost-Benefit 96

Domino Data Lab

AUGUST 10, 2021

Models are the central output of data science, and they have tremendous power to transform companies, industries, and society. At the center of every machine learning or artificial intelligence application is the ML/AI model that is built with data, algorithms and code. The process of creating models is called modeling.

Modeling 64

Modeling IT Risk Management Deep Learning 64

Domino Data Lab

JULY 17, 2021

The system here will identify, via some meaningful sense, which existing speakers’ model does the utterance match. If the unknown utterance is spoken by a speaker outside the list of existing speakers, the model will nonetheless map it to some speaker from that list. It determines whether the individual is who they claim to be.

Modeling 64

Modeling Testing Machine Learning Measurement 64

Jet Global

MAY 15, 2020

A financial Key Performance Indicator (KPI) or metric is a quantifiable measure that a company uses to gauge its financial performance over time. These three statements are data rich and full of financial metrics. The Fundamental Finance KPIs and Metrics – Cash Flow. What is a Financial KPI? Current Ratio. View Guide Now.

Metrics 98

Metrics Finance Reporting KPI 98

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 98

Testing Data Warehouse Metrics Cost-Benefit 98

IBM Big Data Hub

SEPTEMBER 19, 2023

Data science is an area of expertise that combines many disciplines such as mathematics, computer science, software engineering and statistics. Many functions of data analytics—such as making predictions—are built on machine learning algorithms and models that are developed by data scientists.

Data Science 85

Data Science Data Analytics Prescriptive Analytics Analytics 85

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. After the simulation, organizations also receive metrics on employee click rates and often follow up with additional phishing awareness training. million phishing sites.

Testing 82

Testing Measurement Behavioral Analytics Reporting 82

O'Reilly on Data

MARCH 31, 2020

All you need to know for now is that machine learning uses statistical techniques to give computer systems the ability to “learn” by being trained on existing data. This has serious implications for software testing, versioning, deployment, and other core development processes. Machine learning adds uncertainty.

Management 332

Management Machine Learning Experimentation Metrics 332

Smarten

JUNE 29, 2018

This article discusses the Paired Sample T Test method of hypothesis testing and analysis. What is the Paired Sample T Test? The Paired Sample T Test is used to determine whether the mean of a dependent variable e.g., weight, anxiety level, salary, reaction time, etc., is the same in two related groups.

Business Analysis 40

Business Analysis Testing Statistics Advertising 40

Domino Data Lab

AUGUST 22, 2019

The excerpt covers how to create word vectors and utilize them as an input into a deep learning model. While the field of computational linguistics, or Natural Language Processing (NLP), has been around for decades, the increased interest in and use of deep learning models has also propelled applications of NLP forward within industry.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

Smarten

APRIL 12, 2023

Smarten CEO, Kartik Patel says, ‘Smarten SnapShot supports the evolving role of Citizen Data Scientists with interactive tools that allow a business user to gather information, establish metrics and key performance indicators.’

Snapshot 52

Snapshot Key Performance Indicator KPI Business Intelligence 52

Smarten

APRIL 19, 2024

OpenAI – Azure OpenAI as the foundational entity for creating GPT models and is based on Large Language Models (LLM). GPT – Is based on a Large Language Model (LLM). Benefits include customized and optimized models, data, parameters and tuning. Open AI was developed by Microsoft.

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

Andrew White

MARCH 28, 2019

an analytic or metric), or some other more sophisticated representative of a model or algorithm. The article highlights a dialog between scientists and pundits who use ‘ statistical significance ’ as a proof of a fact, argument or casual relationship between factors. The difficult part here is with ‘root’ and ‘founded in’.

Data-driven 79

Data-driven Statistics Testing Metrics 79

The Unofficial Google Data Science Blog

JANUARY 16, 2018

We present data from Google Cloud Platform (GCP) as an example of how we use A/B testing when users are connected. Experimentation on networks A/B testing is a standard method of measuring the effect of changes by randomizing samples into different treatment groups. These contribute to the structure and size of components.

Testing 58

Testing Experimentation Measurement Modeling 58

IBM Big Data Hub

JULY 6, 2023

Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming. Ultimately, data science is used in defining new business problems that machine learning techniques and statistical analysis can then help solve.

Machine Learning 60

Machine Learning Data Science Statistics Deep Learning 60

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

Others argue that there will still be a unique role for the data scientist to deal with ambiguous objectives, messy data, and knowing the limits of any given model. With those stakes and the long forecast horizon, we do not rely on a single statistical model based on historical trends.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

datapine

MAY 17, 2021

Without a doubt, the best way to drive maximum value from the metrics, insights, and information is through something called data discovery. What is a discovery model, and how do you use it in a real-world business context? Your Chance: Want to test a professional data discovery tool for free? What is a data discovery platform?

Visualization 161

Visualization Data-driven Business Intelligence Metrics 161

O'Reilly on Data

MAY 27, 2020

Under school district policy, each of Audrey’s eleven- and twelve-year old students is tested at least three times a year to determine his or her Lexile, a number between 200 and 1,700 that reflects how well the student can read. They test each student’s grasp of a particular sentence or paragraph—but not of a whole story.

Risk 355

Risk Testing Reporting Measurement 355

Domino Data Lab

APRIL 21, 2021

We’ll use a gradient boosting technique via XGBoost to create a model and I’ll walk you through steps you can take to avoid overfitting and build a model that is fit for purpose and ready for production. from sklearn import metrics. Let’s also look at the basic descriptive statistics for all attributes.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

Smarten

DECEMBER 3, 2023

It also augments the expert and citizen data scientists by automating many aspects of data science, machine learning, and AI model development, management and deployment.’ ‘You What is self-service analytics? We should probably explain before we move on. Augmented Analytics vs Predictive Analytics is not really a question.

Key Performance Indicator 52

Key Performance Indicator Analytics IT Predictive Analytics 52

DataKitchen

JUNE 24, 2021

One of the keys for our success was really focusing that effort on what our key business initiatives were and what sorts of metrics mattered most to our customers. Chapin also mentioned that measuring cycle time and benchmarking metrics upfront was absolutely critical. “It GE formed its Digital League to create a data culture.

ROI 211

ROI Metrics Measurement Cost-Benefit 211

Cloudera

DECEMBER 3, 2021

For example, training an interview screening model using education data often contains gender information. As discussed in this article , model design can also be a source of bias too. Even something as simple as choosing a loss function can change the bias of a trained model. Model Drift. Impact and confidence intervals.

Uncertainty 99

Uncertainty Measurement Metrics Risk 99

Octopai

OCTOBER 26, 2022

Continuous pipeline monitoring with SPC (statistical process control). SPC is the continuous testing of the results of automated manufacturing processes. products or product components) are checked to make sure that they do not deviate in a statistically significant way from the expected results. Results (i.e.

Data Quality 52

Data Quality Data Analytics Analytics Manufacturing 52