DataRobot Blog

SEPTEMBER 1, 2022

These and many other questions are now on top of the agenda of every data science team. This also shows how the models compare on standard performance metrics and informative visualizations like Dual Lift. Here the DataRobot view shows that the Challenger beats the Champion on some metrics, but not all.

Metrics 145

Metrics Statistics Modeling Data Science 145

Domino Data Lab

JULY 28, 2021

Data science is a field at the convergence of statistics, computer science and business. Its value is so significant that scaling data science has become the new business imperative with organizations spending tens of millions of dollars on data, technology and talent. Data Science Techniques.

Data Science 45

Data Science Machine Learning Statistics Deep Learning 45

IBM Big Data Hub

NOVEMBER 29, 2023

For instance, if a business prioritizes accuracy in generating synthetic data, the resulting output may inadvertently include too many personally identifiable attributes, thereby increasing the company’s privacy risk exposure unknowingly.

Metrics 83

Metrics Machine Learning Risk Statistics 83

Domino Data Lab

JULY 22, 2019

Paco Nathan presented, “Data Science, Past & Future” , at Rev. This blog post provides a concise session summary, a video, and a written transcript. data science’s emergence as an interdisciplinary field – from industry, not academia. He was saying this doesn’t belong just in statistics.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

datapine

MARCH 31, 2022

According to the US Bureau of Labor Statistics, demand for qualified business intelligence analysts and managers is expected to soar to 14% by 2026, with the overall need for data professionals to climb to 28% by the same year. A background in (or a firm grasp of) data warehousing and mining.

Business Intelligence 239

Business Intelligence Statistics Visualization Data-driven 239

IBM Big Data Hub

DECEMBER 19, 2023

Anomaly detection simply means defining “normal” patterns and metrics—based on business functions and goals—and identifying data points that fall outside of an operation’s normal behavior. The challenge for IT departments working in data science is making sense of expanding and ever-changing data points.

Machine Learning 67

Machine Learning Optimization Unstructured Data Sales 67

Sisense

SEPTEMBER 3, 2020

We live in a constantly-evolving world of data. That means that jobs in data big data and data analytics abound. The wide variety of data titles can be dizzying and confusing! Programming and statistics are two fundamental technical skills for data analysts, as well as data wrangling and data visualization.

Statistics 97

Statistics Metrics Visualization Finance 97

Sisense

APRIL 10, 2020

In Talking Data , we delve into the rapidly evolving worlds of Natural Language Processing and Generation. Text data is proliferating at a staggering rate, and only advanced coding languages like Python and R will be able to pull insights out of these datasets at scale. NLP can be used on written text or speech data.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

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

The Unofficial Google Data Science Blog

DECEMBER 14, 2021

by AMIR NAJMI & MUKUND SUNDARARAJAN Data science is about decision making under uncertainty. 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.

Uncertainty 56

Uncertainty Statistics Measurement Cost-Benefit 56

The Unofficial Google Data Science Blog

OCTOBER 31, 2016

By PATRICK RILEY For a number of years, I led the data science team for Google Search logs. We were often asked to make sense of confusing results, measure new phenomena from logged behavior, validate analyses done by others, and interpret metrics of user behavior. On the flip side, you sometimes have a small volume of data.

Metrics 107

Metrics Measurement Statistics Data Collection 107

The Unofficial Google Data Science Blog

JANUARY 14, 2016

by AMIR NAJMI Running live experiments on large-scale online services (LSOS) is an important aspect of data science. Because individual observations have so little information, statistical significance remains important to assess. We must therefore maintain statistical rigor in quantifying experimental uncertainty.

Experimentation 41

Experimentation Statistics Metrics Measurement 41

Cloudera

FEBRUARY 25, 2021

Today’s enterprise data science teams have one of the most challenging, yet most important roles to play in your business’s ML strategy. Additionally, the AMPs catalog within CML is completely customizable, enabling your data science teams to build and securely share internal projects as AMPs.

Deep Learning 81

Deep Learning Machine Learning Forecasting Visualization 81

Sisense

JANUARY 21, 2020

Everyone wants to get more out of their data, but how exactly to do that can leave you scratching your head. One of the simplest ways to start exploring your data is to aggregate the metrics you are interested in by their relevant dimensions. So how do we get totally different results when breaking the data down by gender?

Testing 104

Testing Risk Data-driven Statistics 104

IBM Big Data Hub

AUGUST 7, 2023

Therefore, IBM observes that more clients tend to consult AI leaders to help establish governance and enhance AI and data science capabilities, an operating model in the form of co-delivery partnerships. The post Optimizing clinical trial site performance: A focus on three AI capabilities appeared first on IBM Blog.

Optimization 57

Optimization Forecasting Data-driven Strategy 57

Jen Stirrup

JUNE 20, 2020

I also want to sound a few warning bells and I have the data to back it up. In efficient data science, we need to take our biases off the table in order to get a true picture of the data we are looking at. In this blog post, I am going to try to help you and me to understand, using PASS as an example.

Metrics 104

Metrics Business Analytics Data-driven Statistics 104

Domino Data Lab

JULY 17, 2021

While more advanced models for speaker verification exist, this blog will form a basis of speech signal processing. NOTE : The scope of this blog is a machine learning approach to TISV. The likelihood function measures the goodness of fit of a statistical model to a sample of data for given values of the unknown parameters.

Modeling 64

Modeling Testing Machine Learning Measurement 64

Sisense

SEPTEMBER 25, 2020

A data scientist must be skilled in many arts: math and statistics, computer science, and domain knowledge. No matter your skill, career level, or title, the ability to analyze, organize, and visualize data are vital skills in our world of quickly growing and ever-changing data. Linear regression.

Statistics 98

Statistics Predictive Modeling Modeling Machine Learning 98

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., This blog post discusses such a comprehensive approach that is used at Youtube. the fraction of video recommendations resulted in positive user experiences).

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

The Unofficial Google Data Science Blog

SEPTEMBER 23, 2015

by KAY BRODERSEN For the first time in the history of statistics, recent innovations in big data might allow us to estimate fine-grained causal effects, automatically and at scale. How can we establish that there is a causal link between our idea and the outcome metric we care about? Is a randomized experiment possible?

Advertising 47

Advertising Statistics Big Data Modeling 47

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

by THOMAS OLAVSON Thomas leads a team at Google called "Operations Data Science" that helps Google scale its infrastructure capacity optimally. It also owns Google’s internal time series forecasting platform described in an earlier blog post. Our team does a lot of forecasting. Our team does a lot of forecasting.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

The Unofficial Google Data Science Blog

JUNE 30, 2016

Identification We now discuss formally the statistical problem of causal inference. We start by describing the problem using standard statistical notation. The field of statistical machine learning provides a solution to this problem, allowing exploration of larger spaces. The annals of statistics (1991): 1-67.

Statistics 44

Statistics Optimization Modeling Experimentation 44

Domino Data Lab

APRIL 21, 2021

In contrast, the decision tree classifies observations based on attribute splits learned from the statistical properties of the training data. Machine Learning-based detection – using statistical learning is another approach that is gaining popularity, mostly because it is less laborious. from sklearn import metrics.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

DataKitchen

MAY 10, 2021

And the worst part – data errors take the fun out of data science. Remember your first data science courses? You probably imagined your career would be about helping drive insights with data instead of having to sit in endless meetings discussing analytics errors and painstaking corrective actions.

Testing 214

Testing Manufacturing Data Quality Statistics 214

The Unofficial Google Data Science Blog

JANUARY 16, 2018

These differences tend to increase the variance of our estimates and hence lose us statistical power. These two properties were selected because they correlate strongly with experiment metrics of greatest interest. Consider the case where experiment metrics are evaluated at the per-user level. Assume we have $K$ users.

Testing 58

Testing Experimentation Measurement Modeling 58

The Unofficial Google Data Science Blog

AUGUST 27, 2019

Of course, any mistakes by the reviewers would propagate to the accuracy of the metrics, and the metrics calculation should take into account human errors. If we could separate bad videos from good videos perfectly, we could simply calculate the metrics directly without sampling. The missing verdicts create two problems.

Metrics 98

Metrics Statistics Uncertainty Optimization 98

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

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. This comes down to model risk management.

Modeling 64

Modeling IT Risk Management Deep Learning 64

Peter James Thomas

AUGUST 12, 2018

This piece was prompted by both Olaf’s question and a recent article by my friend Neil Raden on his Silicon Angle blog, Performance management: Can you really manage what you measure? However – as is often the case with issues I deal with on this blog – fact-based decision-making is easier to say than it is to achieve.

Metrics 49

Metrics Statistics Data Quality Measurement 49

The Unofficial Google Data Science Blog

JULY 18, 2023

That’s the focus of this blog post. We’ll start by defining some key concepts around measuring the quality of human labeled data. Once we’ve answered that, we will then define and use metrics to understand the quality of human-labeled data, along with a measurement framework that we call Cross-replication Reliability or xRR.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

IBM Big Data Hub

DECEMBER 20, 2023

ML is a computer science, data science and artificial intelligence (AI) subset that enables systems to learn and improve from data without additional programming interventions. AI studio The post Five machine learning types to know appeared first on IBM Blog. What is machine learning? Explore the watsonx.ai

Machine Learning 77

Machine Learning Modeling Deep Learning Predictive Modeling 77

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

Domino Data Lab

MAY 8, 2019

The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Instead, consider a “full stack” tracing from the point of data collection all the way out through inference. Other good related papers include: “ Towards A Rigorous Science of Interpretable Machine Learning ”.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Cloudera

SEPTEMBER 17, 2020

For enterprise organizations, managing and operationalizing increasingly complex data across the business has presented a significant challenge for staying competitive in analytic and data science driven markets. For further analysis, stage level summary statistics show the number of parallel tasks and I/O distribution.

Visualization 102

Visualization Metrics Statistics Optimization 102

Smart Data Collective

JANUARY 31, 2022

a website design company, both quantitative and qualitative data should be utilized during a website design or redesign. Quantitative data provides the statistics of an event on a website in number, while qualitative data highlights a number of reasons the event might take place. Elements of data-driven web design.

Data-driven 101

Data-driven Big Data Statistics Optimization 101

Andrew White

JANUARY 9, 2022

But I would search on our web site for AI and mid-size enterprise; or ask Anthony Mullen who leads our AI research in our data and analytics team. Can you remind where we can find the mentioned blog? Can you provide a link to your blog, or will we get notice of your info posted through email? What is an Ecosystem?

Analytics 64

Analytics Measurement Modeling Data-driven 64

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

by TAMAN NARAYAN & SEN ZHAO A data scientist is often in possession of domain knowledge which she cannot easily apply to the structure of the model. On the one hand, basic statistical models (e.g. This blog post motivates this problem more fully, and discusses monotonic splines and lattices as a solution.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108