Smarten

JANUARY 3, 2019

Citizen Analysts create and generate data models and use sophisticated analytics that are supported by easy-to-use interactive BI dashboards. By definition, Citizen Analysts are not data scientists, or professional analysts or IT staff. ’ Clearly, Citizen Analysts are here to stay!

Business Intelligence 40

Business Intelligence Statistics Interactive Data Science 40

The Data Visualisation Catalogue

APRIL 4, 2019

Then in around 2016, I first started using VR hardware and from there I had two thoughts: first, that VR is going to be the most revolutionary technology of my lifetime; and second, that VR can make the process of data analysis and presentation much easier (especially in my job as an investment analyst).

Dashboards 73

Dashboards Predictive Modeling Visualization Modeling 73

Occam's Razor

MARCH 30, 2017

While the phrase Artificial Intelligence has been around since the first human wondered if she could go further if she had access to entities with inorganic intelligence, it truly jumped the shark in 2016. trillion pictures in 2016. Most Deep Learning methods involve artificial neural networks, modeling how our bran works.

Marketing Analytics 166

Marketing Analytics Marketing Analytics Deep Learning 166

Ontotext

MAY 26, 2021

Our first data source says that on June 13, 2016 Microsoft bought LinkedIn , while the second – that Microsoft bought LinkedIn for $28.1 Beyond training, each and every AI system, be it based on symbolic rules or statistical models, has to be evaluated, in the same way children take exams to graduate.

Data Quality 87

Data Quality Machine Learning Measurement Metadata 87

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

Rita Sallam

JULY 22, 2016

Gartner revamped the BI and Analytics Magic Quadrant in 2016 to reflect the mainstreaming of this market disruption. Not sure about that, but Sisense is well suited for easily harmonizing, combining and modeling many different, complex and large data sets for fast interactive analysis. Answer: Better than every other vendor?

Analytics 40

Analytics Business Intelligence Metadata Statistics 40

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. Figure 2: Spreading measurements out makes estimates of model (slope of line) more accurate. And sometimes even if it is not[1].)

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

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

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

Occam's Razor

SEPTEMBER 13, 2016

Bonus #2: The Askers-Pukers Business Model. Econsultancy/Lynchpin provides this description in the report: "There were 960 respondents to our research request, which took the form of a global online survey fielded in May and June 2016. Ok, maybe statistical modeling smells like an analytical skill. Bottom-line.

Modeling 127

Modeling Reporting Strategy Consulting 127

Decision Management Solutions

MARCH 31, 2023

Decision Model and Notation (DMN) is an open industry standard, designed to be a common and standard bridge between what you need a business decision to do and the decision implementation for how it actually happens. Structural Principles TDM’s structural principles (1-7) are around the representation of tabular logic.

Modeling 52

Modeling Statistics Management Technology 52

Domino Data Lab

AUGUST 8, 2019

Meanwhile, many organizations also struggle with “late in the pipeline issues” on model deployment in production and related compliance. Having participated in several Foo Camps—and even co-chaired the Ed Foo series in 2016-17— most definitely, a Foo will turn your head around. Rinse, lather, repeat—probably each week.

Data Science 71

Data Science Machine Learning Data Governance Statistics 71

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

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. Jake Vanderplas (2016). Chris Albon (2018). Think Stats.

Data Science 56

Data Science Machine Learning Reporting Visualization 56

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

Domino Data Lab

OCTOBER 23, 2019

As a result, there has been a recent explosion in individual statistics that try to measure a player’s impact. This chapter will explore the numbers behind the numbers using ML and then creating an API to serve out the ML model. All of this will be done in the spirit of solving real-world problems in a real-world way. In Figure 6.1,

Statistics 59

Statistics Machine Learning Testing Modeling 59

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. On the other hand, sophisticated machine learning models are flexible in their form but not easy to control.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

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

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

datapine

NOVEMBER 19, 2019

Currently, popular approaches include statistical methods, computational intelligence, and traditional symbolic AI. This feature hierarchy and the filters that model significance in the data, make it possible for the layers to learn from experience.

Technology 279

Technology Internet of Things IT IoT 279

The Unofficial Google Data Science Blog

MARCH 13, 2017

Typically, causal inference in data science is framed in probabilistic terms, where there is statistical uncertainty in the outcomes as well as model uncertainty about the true causal mechanism connecting inputs and outputs. We are all familiar with linear and logistic regression models.

IT 68

IT Visualization Modeling Uncertainty 68

datapine

AUGUST 29, 2022

It was lately revised and updated in January 2016. With a very strong practical focus “Analytics in a Big Data World” starts by providing the readers with the basic nomenclature, the analytics process model, and its relation to other relevant disciplines, such as statistics, machine learning, and artificial intelligence.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

datapine

DECEMBER 28, 2021

1) What Is A Misleading Statistic? 2) Are Statistics Reliable? 3) Misleading Statistics Examples In Real Life. 4) How Can Statistics Be Misleading. 5) How To Avoid & Identify The Misuse Of Statistics? If all this is true, what is the problem with statistics? What Is A Misleading Statistic?

Statistics 363

Statistics Advertising Visualization Data mining 363

Domino Data Lab

SEPTEMBER 25, 2019

A big part of statistics, particularly for financial and econometric data, is analyzing time series, data that are autocorrelated over time. One of the most common ways of fitting time series models is to use either autoregressive (AR), moving average (MA) or both (ARMA). Chapter Introduction: Time Series and Autocorrelation.

Forecasting 87

Forecasting Modeling Statistics Optimization 87

Domino Data Lab

MARCH 27, 2019

This data set contains the percentage of votes that were cast in each state for the Democratic Party candidate in each presidential election from 1932 to 2016. Using the summarize() function to calculate summary statistics for the presidentialElections data frame. For more information, see the pscl package reference manual , or use ?

Statistics 85

Statistics Data Science Visualization IT 85

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