How COVID-19 is Changing our Relationship with Data
How COVID-19 is Changing our Relationship with Data An increasing proportion of businesses use scientific methods to analyze data. Yet, because key decision-makers do not...
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Why we need more Bayesian trained data scientists than frequentist post COVID 19 ..
Earlier this week, I was speaking at an event on AI for Real Estate where I showed an example from a BBC clip which said that “central London is now a ghost town” (du...
5 common causes of friction between data scientists and the rest of the stakeholders
Overview: As a data scientist, have you ever been frustrated that your stakeholders don’t see the value that you bring to the table? You may ask yourself, “How far sh...
DSC Webinar Series: DataOps: How Bell Canada Powers their Business with Data
Agile data management has become a necessity for organizations that need to maximize the value of their data. The business is focused on outcomes but the bulk of the effo...
Introducing the “4 Stages of Data Monetization”
Ah, the fond memories of the early days of Saturday Night Live and the hilarious “Point / Counterpoint” debates between Jane Curtin and Dan Aykroyd. I can just imagin...
Overcoming an Imbalanced Dataset using Oversampling.
How oversampling yielded great results for classifying cases of Sexual Harassment. The problem: Overcoming an imbalanced data set When it comes to data science, sexual ha...
Data Decay and Data Enrichment
Data, an organization’s intellectual asset, must be treated and regularly enriched to remain useful and valuable. Over 80% of companies we’ve worked with, — includi...
How to Communicate Data
The following graphic is based on Sam Priddy’s excellent DSC/Tableau Webinar How to Accelerate and Scale Your Data Science Workflows. Sam covered many interesting ...
Data Governance: Bottom Up Approach to break silos
Over a period, large organizations have been transformed into disintegrated silos that has grown to be a major impediment to respond to changing market demands with agili...
23 sources of data bias for #machinelearning and #deeplearning
In the paper A survey on bias and fairness in machine learning.- the authors outline 23 types of bias in data for machinelearning. The source is good – so below is an...