Data Science Conversations Damien Deighan and Philipp Diesinger

In this episode Patrick McQuillan shares his innovative Biological Model - a concept you can use to enhance data outcome in large enterprises.  The concept takes the idea that the best way to design a data strategy is to align it closely with a biological system.
He discusses the power of centralized information, importance of data governance, and the necessity for a common performance narrative across an organization.
Episode Summary -
- Biological Model Concept
- Centralized vs. Decentralized Data
- Data Collection and Maturity
- Horizontal translation layer 
- Partnership with vertical leaders
 - Curated data layers 
- Data dictionary for consistency
- Focusing on vital metrics
- Data Flow in Organizations
- Biological Model Governance
- Overcoming Inconsistency and Inaccuracy

In this episode Heidi Hurst returns to talk to us about how in her current role at Pachama she is using the power of machine learning to fight climate change.  She discusses her work in measuring the capacity of existing forests and reforestation projects using satellite imagery.
Episode Summary
1. The importance of carbon credits verification in mitigating climate change
2. How Pachama is using machine learning and satellite imagery to verify carbon projects
3. Three types of carbon projects: avoided deforestation, reforestation, and improved forest management
4. Challenges in using satellite imagery to measure the capacity of existing forests
5. The role of multispectral imaging in measuring density of forests
6. Challenges in collecting data from dense rainforests and weather obstructions
7. The impact of machine learning on scaling up carbon verification
8. Advancements in the field of satellite imaging, particularly in small satellite constellations

Ágnes Horvát is an Assistant Professor in Communication and Computer Science at Northwestern University. Her work focuses on understanding how online networks induce biased information production, sharing and processing across digital platforms. 
- The new Post-normal era for science - Having an awareness of the context and values that impact scientific research

Where is science communication in relation to digital platforms? - Scholars work hard on discovering scientific findings, but information doesn’t always reach the public appropriately.  

How to communicate scientific research - it’s not just about communicating with scientists and general audiences. News needs to reach policymakers and governments too for real change.

The production of scientific research has exploded recently thanks to decision-making demands - and the pandemic had a lot to do with this. Scientists were under pressure to carry out research quickly and at the expense of quality. 

Misinformation can have detrimental consequences - even leading to vaccine hesitancy in some communities.

The surprising effect of retracting papers - papers that get retracted in the future are more likely to receive more engagement before getting withdrawn.

Why are paper retractions on the rise?  - again, the recent pandemic has caused an increase in retractions. 

Is social media helping or hindering science research? - while the platforms are helping to  spread real news, social media also helps the spread of false information. 

As long as you have quality data and robust trends - regardless of the method, you will identity that trend. 

Reducing the problem of miscommunication - with whom does the responsibility lie?

Modern Data Infrastructures and platforms store huge amounts of multidimensional data.  But - data pipelines frequently break and a machine learning algorithm's performance is only as good as the quality and reliability of the data itself.
In this episode we are joined by Lior Gavish and Ryan Kearns of Monte Carlo, to talk about how the new concept of Data Observability is advancing Data Reliability and Data Quality at Scale.

Episode Summary

A overview of Data Reliability/Quality and why it is so critical for organisationsThe limitations of traditional approaches in the area of Data ReliabilityData observability and why it is different to traditional approaches to Data QualityThe 5 Pillars of Data ObservabilityHow to improve data reliability/quality at scale and generate trust in data with stakeholders.How observability can lead to better outcomes for Data Science and engineering teams?Examples of data observability use cases in industryOverview of O’Reilly’s upcoming book, The Fundamentals of Data Quality.

In this episode we are joined by Julia Stoyanovich from NYU, to talk about her work into how AI is being used in the hiring process.
Whether you are responsible for hiring on behalf of a business or are a job seeker, you will find this podcast very interesting, but for very different reasons.
Episode Summary
Algorithmic decision making in the hiring process - what does that mean for businesses and job seekers?The hiring process - the funnel effect.Lack of public disclosure about the use of algorithmic tools as part of the talent acquisition pipeline.Are job seekers being unfairly screened out of the hiring process?How AI based implementations of psychometric instruments are used today.Is it possible to measure a person’s personality based on data alone?Do these systems remove bias and discrimination from the hiring process?Testing the stability and consistency of these algorithmic systems.Vendors of systems and their lack of testing / recognising the issues.Are new laws needed so the hiring process is fairer and more transparent?What does the future of hiring look like - fewer AI systems and more human intervention?

In this episode we are joined by Professor Maurizio Porfiri from NYU, to talk about his latest academic research which is using data science to uncover why sales of guns in the USA increase after a mass shooting event.  
His interest and research was borne from a very personal experience 14 years ago when he experienced a mass shooting event at Virginia Tech where he was studying.
Researching Complex SystemsVirginia Tech Mass Shooting event and its impact on MaurizioWhat is the relationship between mass shooting events and the purchase of guns?Analysis of time series data - 70 mass shootings in around 20 yearsCan media coverage on mass shootings shape public opinion, thereby influencing firearm acquisition?Examining the correlation between three distinct datasetsWhat are the causes of increased gun sales in the aftermath of mass shooting events?Differences in the data at State level V National level?Researching the complex firearm ecosystem with all its pieces - prevalence, violence and regulation