Smarten

NOVEMBER 20, 2023

The field of AI includes planning and learning, natural language processing (NLP) and machine learning, as well as reasoning and perception, neural networks, economics, mathematics and statistics. Its training model is huge, consisting of many terabytes of text data. GPT is an LLM. GPT is open-source. OpenAI offers ChatGPT services.

Technology 52

Technology Digital Transformation Software Consulting 52

Occam's Razor

JANUARY 9, 2012

Our mental model has not shifted enough to the existing reality. AND you can have analysis of your risk in almost real time to get an early read and in a few days with statistical significance! So do the 7 things above, but ensure you have a clearly articulated Digital Marketing & Measurement Model. Got your own?

Marketing 140

Marketing B2B Measurement Risk 140

Smart Data Collective

JUNE 26, 2019

Get Rid of Blind Spots in Statistical Models With Machine Learning. Data-related blind spots could also exist in your statistical models. RiskSpan is a company that built a machine learning algorithm that can flag error-prone parts of a statistical model and indicate which associated outputs may be unreliable.

ROI 102

ROI Statistics Machine Learning Measurement 102

The Unofficial Google Data Science Blog

MARCH 31, 2016

KUEHNEL, and ALI NASIRI AMINI In this post, we give a brief introduction to random effects models, and discuss some of their uses. Through simulation we illustrate issues with model fitting techniques that depend on matrix factorization. Random effects models are a useful tool for both exploratory analyses and prediction problems.

Modeling 40

Modeling Statistics Advertising Testing 40

datapine

SEPTEMBER 27, 2022

Some of these ‘structures’ may include putting all the information; for instance, a structure could be about cars, placing them into tables that consist of makes, models, year of manufacture, and color. 17) “SQL Database Programming” (2015 Edition) By Chris Fehily. It is a must-read for understanding data warehouse design.

Business Intelligence 292

Business Intelligence Data Warehouse Data Processing Data mining 292

Jet Global

AUGUST 4, 2023

The base skills you need to master to conduct analysis in Excel include: Modeling design principles Common functions and formulas Excel Tables Pivot Tables & Pivot Charts Charts & Graphs Learning the above is an entry point and is the start of one’s journey working with and analyzing data. The beauty of Modern Excel is two-fold.

Data Analytics 52

Data Analytics Analytics Finance Visualization 52

Smart Data Collective

MAY 19, 2021

This trend is prevalent in the US, UK and further afield, with the former’s Bureau of Labour Statistics reporting that three of the jobs with the highest forecast growth level are wind turbine technician, registered nurse, and solar technician.

Data Analytics 89

Data Analytics Analytics Big Data Predictive Analytics 89

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. It advances the scalability of ML in real-world applications by using algorithms to improve model performance and reproducibility. Where they are deployed.

Data Science 55

Data Science Machine Learning Cost-Benefit Deep Learning 55

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. One reason to do ramp-up is to mitigate the risk of never before seen arms.

Experimentation 95

Experimentation Statistics Testing Strategy 95

CIO Business Intelligence

JULY 6, 2023

Spreading the news Telecom provider AT&T began trialing RPA in 2015 to decrease the number of repetitive tasks, such as order entry, for its service delivery group. GPT-4 (Generative Pre-trained Transformer 4) is a multimodal large language model created by OpenAI, an AI research laboratory. Secure sponsorship.

Cost-Benefit 115

Cost-Benefit Technology Unstructured Data Testing 115

Peter James Thomas

AUGUST 18, 2018

From 2000 to 2015, I had some success [5] with designing and implementing Data Warehouse architectures much like the following: As a lot of my work then was in Insurance or related fields, the Analytical Repositories tended to be Actuarial Databases and / or Exposure Management Databases, developed in collaboration with such teams.

Data Lake 63

Data Lake Data Warehouse Data mining Statistics 63

The Unofficial Google Data Science Blog

JULY 11, 2017

SCOTT Time series data are everywhere, but time series modeling is a fairly specialized area within statistics and data science. This post describes the bsts software package, which makes it easy to fit some fairly sophisticated time series models with just a few lines of R code. by STEVEN L. Forecasting (e.g.

Forecasting 64

Forecasting Statistics Modeling Software 64

Alation

FEBRUARY 20, 2020

To address these needs, a catalog should provide data samples and statistical profiles, lineage, lists of users and stewards, and tips on how the data should be interpreted. LinkedIn provides a good model. Prescriptive vs. Descriptive. This method is also slow, and the documentation often gets stale. The Bottom Line.

Metrics 52

Metrics Consulting Machine Learning Statistics 52

Rita Sallam

APRIL 2, 2023

Interpretation of our machine learning model suggests buying $50k in building coverage and $100k in warehouse content coverage, as the latter significantly boosts predicted flood claim payouts. When exploring river and coastal flooding for the USA on a city level, river flooding area had a drastic increase starting from 2015.

Optimization 52

Optimization Machine Learning Insurance Risk 52

Cloudera

MARCH 2, 2018

Banking, transportation, healthcare, retail, and real estate, all have seen the emergence of new business models fundamentally changing how customers use their services. Bridgespan Group estimated in 2015 that only 6% of nonprofits use data to drive improvements in their work.

Data-driven 43

Data-driven Prescriptive Analytics Descriptive Analytics Machine Learning 43

O'Reilly on Data

DECEMBER 9, 2019

Recall from my previous blog post that all financial models are at the mercy of the Trinity of Errors , namely: errors in model specifications, errors in model parameter estimates, and errors resulting from the failure of a model to adapt to structural changes in its environment.

Statistics 27

Statistics Uncertainty Risk Marketing 27

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 choice of space $cal F$ (sometimes called the model ) and loss function $L$ explicitly defines the estimation problem. For a random sample of units, indexed by $i = 1.

Statistics 44

Statistics Optimization Modeling Experimentation 44

Rita Sallam

JULY 22, 2016

By contrast, traditional BI platforms are designed to support modular development of IT-produced analytic content, specialized tools and skills, and significant upfront data modeling, coupled with a predefined metadata layer, is required to access their analytic capabilities. Answer: Better than every other vendor?

Analytics 40

Analytics Business Intelligence Metadata Statistics 40

bridgei2i

MAY 26, 2020

You know the markets shake and the accompanying Swine Flu epidemic of 2015 and 2016, the Japanese tsunami and the Thailand floods in 2011 that shook up the high-tech value chain quite a bit, the great financial crisis and the accompanying H1N1 outbreak in 2008-2009, MERS and SARS before that in 2003. As the crisis evolved.

Enterprise 131

Enterprise Management Forecasting Manufacturing 131

Domino Data Lab

JUNE 8, 2021

Support Vector Machines (SVMs) are supervised learning models with a wide range of applications in text classification (Joachims, 1998), image recognition (Decoste and Schölkopf, 2002), image segmentation (Barghout, 2015), anomaly detection (Schölkopf et al., 1999) and more. References. Barghout, L. Pedrycz & S.-M. Rouveirol (Eds.),

Optimization 76

Optimization Machine Learning Measurement Data Science 76

The Unofficial Google Data Science Blog

MAY 31, 2016

Statistical power is traditionally given in terms of a probability function, but often a more intuitive way of describing power is by stating the expected precision of our estimates. This is a quantity that is easily interpretable and summarizes nicely the statistical power of the experiment.

Advertising 42

Advertising Testing Sales Statistics 42

Occam's Razor

JULY 13, 2017

5: What-if Analysis Models. allow for smart elements like modeling. Strategy 5: What-if Analysis Models. Building on the thought above, if you create exploratory environments it can be exceedingly accretive to decision-making if we build in what-if type models. 2: If Complex, Focus! 3: Venn Diagrams FTW! That’s the key.

Visualization 124

Visualization Strategy Interactive Statistics 124

Domino Data Lab

JULY 9, 2019

Diving into examples of building and deploying ML models at The New York Times including the descriptive topic modeling-oriented Readerscope (audience insights engine), a prediction model regarding who was likely to subscribe/cancel their subscription, as well as prescriptive example via recommendations of highly curated editorial content.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

Occam's Razor

MARCH 30, 2017

Most Deep Learning methods involve artificial neural networks, modeling how our bran works. There are some who believe that anything modeled on the human brain, like Deep Learning, will be limited in it’s intelligence, that it will inherit the limits and flaws that our intelligence possesses. Yes, a silo but so much better than 2015.

Marketing Analytics 166

Marketing Analytics Marketing Analytics Deep Learning 166

Andrew White

JANUARY 9, 2022

A data fabric has the same model but it adds a set of capabilities that continuously monitor the whole landscape, and continuously update the intelligent part that infers insight as new data is discovered and/or added. This was not statistic and we have not really explored this in any greater detail since. I suspect we should.

Analytics 64

Analytics Measurement Modeling Data-driven 64

Domino Data Lab

JANUARY 6, 2019

Also, clearly there’s no “one size fits all” educational model for data science. Laura Noren, who runs the Data Science Community Newsletter , presented her NYU postdoc research at JuptyerCon 2018, comparing infrastructure models for data science in research and education. Joel Grus (2015). Pedro Domingos (2015).

Data Science 56

Data Science Machine Learning Reporting Visualization 56