How Does Synergistic Data Science Benefit Analyses Within the Scientific Community?

OUTLINE:

1. Cross-Disciplinary

2. Cross-Paradigm

3. Cross-Method

4. Yields (positive synergy only):

a. 2+2 > 4

b. 3×3 > 9

No mistake in the math. The whole is greater than the sum of its parts.

5. Holistic Power of Synergy. The whole is greater than the sum of its parts.

6. Not an “Answer” but an “Answer Set”.

7. Robustness of Results Across Supervised and Non-Supervised Learning.

8. Improved Inference. Also IDs what Metrics or Networks are Most Important.

9. Preserves Scientific Method, but Data can be Mined to Answer Questions We Should Have Thought of …..but do not Know to Ask.

10. Here’s a fundamental post that operationalizes the contribution of synergistic data science to scientific inquiry.

By Dr. Dean G. “Gordy” Fairchild (CEO, Synergy Data Science)

Fundamental Posts

Role Played in Science by Synergistic Data Science

By Dr. Dean G. “Gordy” Fairchild (CEO, Synergy Data Science), poe.com AI (Assistant) Synergistic data science plays a valuable role in scientific disciplines by aiding in the testing of hypotheses and the explanation of phenomena (OS). Synergistic data science helps science by creating answers to questions that the researcher did not ask—but should have (NS). […] Read more

What is Multicollinearity? Why Does it Matter? When can it be Ignored? What Models are Affected? How is it Diagnosed and Corrected?

Dr. Gordy Fairchild (CEO of Synergy Data Science) Misinformation about multicollinearity is commonplace. In short, there is disagreement among practitioners about many facets of multicollinearity. Both new-school and old-school data scientists often handle it in different ways. Inexperienced new-school practitioners who have been trained quickly in a data science boot camp typically do not have […] Read more

A Data Scientist’s Look at Synergistic Data Science

Dr. Gordy Fairchild, CEO SynergyData.science & queries from Poe AI. Synergistic data science refers to the integration and collaboration of different disciplines, industries, and techniques within the field of data science to achieve enhanced results and insights. It involves combining various methodologies, tools, and expertise from domains such as economics, statistics, machine learning, computer science, […] Read more

General Posts

AI and Productivity in Software Development-Thoughts and Comments

In my first post for the new synergy.science blog, I concentrate on a very recent article about the salient issue in an AI-driven labor and occupational restructuring that may be imminent in software development. This article is available for a no-charge download. Here are some details: “The Effects of Generative AI on High Skilled Work: […] Read more

Are outliers bad news for data scientists? Should they be thrown out of modeling datasets or models themselves?

By Dr. Dean G. “Gordy” Fairchild (CEO, Synergy Data Science), poe.com AI (Assistant), poe.com Data-Scientist-GPT3, Univ of California Berkely Statistics Glossary, Data Camp Data Science Glossary The answer to both questions depends on old-school vs. new-school viewpoint as well as the context of the problem and what type of outlier is being described. One thing […] Read more

Data Reliability and Validity, Redux

August 1 2023 By Dr. Bill Luker (Synergy Data Science) Here is a recent post, from the vast LinkedIn commentariat, that raises the often neglected issue of reliability and analytic validity (R&V) in survey data. Since survey data constitutes such a huge proportion of all data collected by business and academic research scientists, it’s important. […] Read more

How do you set the optimal cut-off (probability threshold) for classifiers without blindly using the default of 0.5?

Setting probability thresholds in classification models depends on the specific requirements of your problem and the trade-offs you are willing to make between precision and recall (aka sensitivity or the True Positive Rate. In binary and categorical classification, the model typically outputs a probability score for each class, and a threshold is applied to determine […] Read more

What’s in a definition: AI, ML, Data Science, Statistics, Analytics, and Econometrics?

Dr. Gordy Fairchild & Poe AI. While there is some overlap among these terms, they represent distinct fields and approaches. Here’s an overview of the differences among the terms “data science,” “artificial intelligence” (AI), analytics, statistics, “machine learning,” and econometrics. It should be noted that to some practitioners AI includes all the other terms as […] Read more