Domino Data Lab

OCTOBER 7, 2020

Statistical methods for analyzing this two-dimensional data exist. MANOVA, for example, can test if the heights and weights in boys and girls is different. This statistical test is correct because the data are (presumably) bivariate normal. Data Has Properties. P >> N) ).

Statistics 145

Statistics Testing Predictive Modeling Modeling 145

Smarten

JANUARY 30, 2024

Business users cannot even hope to prepare data for analytics – at least not without the right tools. Gartner predicts that, ‘data preparation will be utilized in more than 70% of new data integration projects for analytics and data science.’ So, why is there so much attention paid to the task of data preparation?

Analytics 67

Analytics Data Quality Visualization Metadata 67

IBM Big Data Hub

FEBRUARY 13, 2024

Like diligent students, these generative models soak up information and identify patterns, structures and relationships between data points, which is how they learn the grammar of poetry, artistic brushstrokes and musical melodies. Imagine each data point as a glowing orb placed on a vast, multi-dimensional landscape.

Enterprise 122

Enterprise Modeling Testing Data-driven 122

IBM Big Data Hub

NOVEMBER 29, 2023

They are already identifying and exploring several real-life use cases for synthetic data, such as: Generating synthetic tabular data to increase sample size and edge cases. You can combine this data with real datasets to improve AI model training and predictive accuracy. How to get started with synthetic data in watsonx.ai

Metrics 83

Metrics Machine Learning Risk Statistics 83

Smarten

MAY 29, 2023

World-renowned technology analysis firm Gartner defines the role this way, ‘A citizen data scientist is a person who creates or generates models that leverage predictive or prescriptive analytics, but whose primary job function is outside of the field of statistics and analytics. ‘If

Prescriptive Analytics 52

Prescriptive Analytics Predictive Modeling Digital Transformation Business Objectives 52

Domino Data Lab

JULY 19, 2021

One of the primary challenges of any ML/AI project is transitioning it from the hands of data scientists in the develop phase of the data science lifecycle into the hands of engineers in the deploy phase. Where in the life cycle does data scientists’ involvement end? They may rely on, or be assisted by, the data analysts.

Enterprise 85

Enterprise Data Science Testing Modeling 85

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

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

Smarten

MAY 31, 2022

The augmented analytics solution your business chooses must have a complete set of predictive analytical techniques with built-in, sophisticated algorithms and tools that are easy to use and will guide the user to choose the right predictive analytical technique for the data they are analyzing. Hypothesis Testing. Trends and Patterns.

Predictive Analytics 67

Predictive Analytics Analytics Predictive Modeling Prescriptive Analytics 67

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

Cloudera

SEPTEMBER 10, 2021

The result: Magnitudes of data sets that used to take weeks or months to stitch together and analyze—if the IRS could do so on existing machines at all—now can be processed in days, hours, or even minutes. We need to be able to make accurate decisions at speed while utilizing vast swathes of data. “We

Machine Learning 66

Machine Learning Statistics Data-driven Big Data 66

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

CIO Business Intelligence

OCTOBER 13, 2023

It’s a role that requires experience with natural language processing , coding languages, statistical models, and large language and generative AI models. According to the survey, 28% of respondents said they have hired data scientists to support generative AI, while 30% said they have plans to hire candidates.

Deep Learning 143

Deep Learning Machine Learning Modeling Software 143

Smarten

JUNE 17, 2022

After completion of the testing procedure, the certificate is provided to show that all requirements were met. The Smarten approach to business intelligence and business analytics focuses on the business user and provides Advanced Data Discovery so users can perform early prototyping and test hypotheses without the skills of a data scientist.

Analytics 67

Analytics IT Business Intelligence Predictive Modeling 67

Rita Sallam

APRIL 2, 2023

We explored these questions and more at our Bake-Offs and Show Floor Showdowns at our Data and Analytics Summit in Orlando with 4,000 of our closest D&A friends and family. The first featured analytics and BI platform Gartner Magic Quadrant leaders while the other showcased high interest data science and machine learning platforms.

Optimization 52

Optimization Machine Learning Insurance Risk 52

Smarten

APRIL 12, 2023

About Smarten The Smarten approach to business intelligence and business analytics focuses on the business user and provides Advanced Data Discovery so users can perform early prototyping and test hypotheses without the skills of a data scientist.

Snapshot 52

Snapshot Key Performance Indicator KPI Business Intelligence 52

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

Domino Data Lab

JULY 10, 2019

For example, there have been multiple promising tools created recently that have Python APIs, are built on top of TensorFlow or PyTorch , and encapsulate deep learning best practices to allow data scientists to speed up research. Ludwig can leverage the same data preprocessing and postprocessing on different datasets with common types.

Deep Learning 110

Deep Learning Visualization Recreation/Entertainment Data Processing 110

IBM Big Data Hub

APRIL 18, 2024

Achieving these feats is accomplished through a combination of sophisticated algorithms, natural language processing (NLP) and computer science principles. LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language. What are the types of AGI?

Cost-Benefit 93

Cost-Benefit Modeling Manufacturing Interactive 93

DataKitchen

DECEMBER 30, 2020

Full disclosure: some images have been edited to remove ads or to shorten the scrolling in this blog post. DBTA’s 100 Companies That Matter Most in Data. CRN’s The 10 Hottest Data Science & Machine Learning Startups of 2020 (So Far). Top Executive: Christopher Bergh, CEO. Headquarters: Cambridge, Mass.

Testing 241

Testing Big Data Statistics Manufacturing 241

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

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

Alation

SEPTEMBER 28, 2022

DataOps strategies share these common elements: Collaboration among data professionals and business stakeholders. Easy-to-experiment data development environment. Automated testing to ensure data quality. There are many inefficiencies that riddle a data pipeline and DataOps aims to deal with that. Author Bio.

Cost-Benefit 52

Cost-Benefit Data Quality Manufacturing Testing 52

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. Some people seemed to be naturally good at doing this kind of high quality data analysis. I'd like to share the contents of that document in this blog post. 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

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. It's why estimating causal effects has been at the heart of data science. Some ideas, though, cannot be tested through randomized experiments.

Advertising 47

Advertising Statistics Big Data Modeling 47

Domino Data Lab

JULY 15, 2021

In the multiverse of data science, the tool options continue to expand and evolve. While there are certainly engineers and scientists who may be entrenched in one camp or another (the R camp vs. Python, for example, or SAS vs. MATLAB), there has been a growing trend towards dispersion of data science tools.

Data Science 145

Data Science Visualization Modeling Enterprise 145

O'Reilly on Data

NOVEMBER 28, 2023

Unexpected outcomes, security, safety, fairness and bias, and privacy are the biggest risks for which adopters are testing. Programmers have always developed tools that would help them do their jobs, from test frameworks to source control to integrated development environments. Only 4% pointed to lower head counts. Perhaps not yet.

Enterprise 271

Enterprise Modeling Testing Reporting 271

The Unofficial Google Data Science Blog

JUNE 30, 2016

To do this, you have a data set at the person level containing, among other variables, an indicator of ad exposure, and whether the person bought the truck. A naïve way to solve this problem would be to compare the proportion of buyers between the exposed and unexposed groups, using a simple test for equality of means.

Statistics 44

Statistics Optimization Modeling Experimentation 44

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

APRIL 23, 2024

If $Y$ at that point is (statistically and practically) significantly better than our current operating point, and that point is deemed acceptable, we update the system parameters to this better value. This blog post discusses such a comprehensive approach that is used at Youtube. Indeed, such an approach is tractable and often used.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

The Unofficial Google Data Science Blog

JULY 22, 2020

For example, imagine a fantasy football site is considering displaying advanced player statistics. A ramp-up strategy may mitigate the risk of upsetting the site’s loyal users who perhaps have strong preferences for the current statistics that are shown. We offer two examples where this may be the case.

Experimentation 95

Experimentation Statistics Testing Strategy 95

Domino Data Lab

NOVEMBER 17, 2020

It is hoped this data will give researchers a better understanding of what they are studying. Classical statistics, developed in the 20 th century for small datasets, do not work for data where the number of variables is much larger than the number of samples (Large P Small N, Curse of Dimensionality, or P >> N data).

Statistics 131

Statistics Measurement Testing Predictive Modeling 131

Domino Data Lab

AUGUST 9, 2021

With the right tools, your data science teams can focus on what they do best – testing, developing and deploying new models while driving forward-thinking innovation. In general terms, a model is a series of algorithms that can solve problems when given appropriate data. It’s most helpful in analyzing structured data.

Modeling 111

Modeling Deep Learning Machine Learning Statistics 111

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. 3f" % x) dataDF.describe().

Statistics 69

Statistics Machine Learning Modeling Metrics 69

The Unofficial Google Data Science Blog

DECEMBER 4, 2015

In other cases, features such as standard libraries and a language’s community of users are more important — the main reason that R is a great language for statistics is that it’s widely used by statisticians and has a huge variety of useful statistics libraries.

Statistics 54

Statistics Interactive Software Modeling 54

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

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