Tag Archives: data science

Debunking Common Myths About Data

14 Wed May 2025

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Martyn Richard Jones, Galicia, 14th May 2025.

I’m feeling generous, so I would like to introduce you to my top eight list of foolish things that people say about data.

I think that data does have a role to play in some businesses. I also believe that some of the basic distributed file store and text search technologies used in data can be usefully employed, in non-traditional indexing, counting and correlation. However, there is an awful lot of nonsense said and written about data.

So, onwards and upwards.

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Frequentist Inference Ate My Homework

18 Sat Jan 2025

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Martyn Richard Jones, A Coruña 18th January 2025

Frequentist inference is a type of statistical inference based on frequentist probability, which treats “probability” in equivalent terms to “frequency” and draws conclusions from sample data by means of emphasizing the frequency or proportion of findings in the data. Frequentist inference underlies frequentist statistics, in which the well-established methodologies of statistical hypothesis testing and confidence intervals are founded.

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Choice Modelling

16 Thu Jan 2025

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Martyn Richard Jones – Madrid 16th January 2025

Choice modelling attempts to model the decision process of an individual or segment via revealed preferences or stated preferences made in a particular context or scenario. Typically, it attempts to use discrete choices (A over B; B over A, B & C) in order to infer positions of the items (A, B and C) on some relevant latent scale (typically “utility” in economics and various related fields). 

http://en.wikipedia.org/wiki/Choice_modelling

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Causal Analysis

15 Wed Jan 2025

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Martyn Richard Jones

Causal analysis is the field of experimental design and statistics pertaining to establishing cause and effect. Typically, it involves establishing four elements: correlation, sequence in time (that is, causes must occur before their proposed effect), a plausible physical or information-theoretical mechanism for an observed effect to follow from a possible cause, and eliminating the possibility of common and alternative (“special”) causes. Such analysis usually involves one or more artificial or natural experiments.

https://en.wikipedia.org/wiki/Causal_analysis

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Analysis of variance

08 Wed Jan 2025

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Analysis of variance (ANOVA) is a collection of statistical models and their associated estimation procedures (such as the “variation” among and between groups) used to analyze the differences among means. ANOVA was developed by the statistician Ronald Fisher. ANOVA is based on the law of total variance, where the observed variance in a particular variable is partitioned into components attributable to different sources of variation. In its simplest form, ANOVA provides a statistical test of whether two or more population means are equal, and therefore generalizes the t-test beyond two means. In other words, the ANOVA is used to test the difference between two or more means.

http://en.wikipedia.org/wiki/Analysis_of_variance

Consider this: Analysis of variance (ANOVA) is a statistical method used to compare the means of three or more groups and determine whether there are statistically significant differences between them. It assesses whether the variability among group means exceeds what would be expected due to random variation alone.

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Rethinking Data Warehousing and Team Collaboration

04 Fri Oct 2024

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Data Warehousing means monolithic and siloed teams?

“Great things in business are never done by one person;

they’re done by a team of people.”

Steve Jobs

Martyn Richard Jones, Tours, 4th October 2024

Narrator: There is a widespread belief amongst the know-it-all crowd that data warehousing and business intelligence necessarily mean monolithic and siloed teams. And that the only way of moving away from such team organisations is to kill off data warehousing. But is this really a rational, coherent, and cohesive approach, as some people say it is? Or is it destructive stupidity born out of conceit, ignorance, and arrogance?

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Understanding Exhaust Data in Data Warehousing

02 Wed Oct 2024

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The data warehouse is the place to copy exhaust data to

Martyn Jones, Bilbao, 2nd October 2024

Narrator: Not all data we have is related to strictly business domains such as products, organisation structure and corporate real estate. A lot of data we collect is simply about monitoring all aspects of IT, applications, networking, security and governance. To name just a few.

Dud: What’s all this data in my exhaust? Is my data back-end going well, or do I have a mechanical data governance issue? All this OLTP exhaust data is so tedious and tiring.

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Data Warehousing means monolithic databases?

27 Fri Sep 2024

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Data Warehousing means monolithic databases?

Martyn Jones, Brooklyn, 27th September 2024

Narrator: Another false meme doing the rounds is that Data Warehousing necessarily means monolithic databases. This is not what data warehouses have been for many businesses.

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Free Business Analytics Content –Thanks to Wikipedia – Part 5

17 Thu Mar 2016

Posted by in Analytics, Ask Martyn, Big Data Analytics, data science, Inform, educate and entertain., Martyn does, Martyn Jones, Martyn Richard Jones, statistics

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Why buy when you can get it for free?

Back at you! Here is the fifth fantastic delivery of an amazing and fabulous selection of free and widely available business analytics learning content, which has been prepared… just for you. Continue reading →

The Big Data Shell Game

11 Wed Nov 2015

Posted by in All Data, Big Data, Cloud, Data Lake, data science, Inform, educate and entertain., IoT, Martyn Jones

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To the experienced observer, Big Data propaganda may well appear to be a disorganised surfeit of half-truths, sleights of hand and boloney. Indeed, the once famously alliterative characterisation of Big Data as defined by volumes, variety and velocity, seems now more appropriately applied to the quantity, invariability and quality of the incessant self-aggrandising hype, hokum and Hadoop being astro-turfed by every dog and his guru. Indeed, the very fact that such an inevitable mega-trend needs so much hype, disingenuousness and spin to support its passage to universal applicability, is a massive contradiction, a disservice to professionals, and an artless deception worthy of our criticism and condemnation. Continue reading →