By Dr. Dean G. “Gordy” Fairchild (CEO, Synergy Data Science), poe.com AI (Assistant)
While there is some overlap among these terms, they represent often too-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 subsets of AI. Others would say that Data Science includes AI and all of the other terms below. Much of this depends on old-school vs. new-school practitioner practice and tradition. The new-school vs. old-school glossary contains dozens of other terms from both schools.
Data Science: Data science is an interdisciplinary field that involves extracting insights and knowledge from data through various techniques and methods. It encompasses elements of statistics, programming, data visualization, business judgment, and domain expertise. Data scientists use computational and statistical approaches to analyze small, large, and complex datasets, build predictive models, and solve complex problems.
Synergy Data Science offers a broader Definition:
“There are several definitions, typically based on industry, “science paradigm”, or educational background of data scientists themselves. Data science includes data extraction, data taming, data mining, visualization, modeling, coding, back-testing, deep learning, AI (Artificial Intelligence), price optimization, statistics, and business analytics. Many “new school” data scientists are from paradigms (scientific world views) such as engineering or computer science. Those two fields focus on problems concerning inanimate objects such as computer networks or engineering components. New school methods can produce predictions of business-related human behavior quite accurately. “Old school” data scientists are experienced in forecasting and explaining human behavior. These data scientists bring BI (Business Intelligence), the scientific method, statistics, econometric models, finance, and marketing modeling to applied data science. At first glance, the “new-school” emphasis on inanimate objects contrasts with the “old-school” emphasis on people. But many new-school forms of data science apply well to market and human behavior while old-school methods can provide answers to the behavior of hard science.”
Data science is a relatively new term as is evidenced by the graph below comparing the search terms “data science” and “econometrics”. Econometrics is an old-school term while data science is new-school and the graph shows that data science searches on Google exceeded those for econometrics starting in 2015 and now completely dominate.
Our unique advantage at Synergy Data Science is the “Synergistic Method™ we pioneered to leverage old- and new-school approaches targeted to a customer’s needs. We obtain stronger, more robust, and accurate solutions with our synergistic mixture of “new-school” and “old-school” data scientists. (Synergy Data Science) https://www.SynergyData.science)
Artificial Intelligence (AI): AI refers to the development of computer systems that can perform tasks that typically require human intelligence and machine logic. It involves creating intelligent machines capable of learning, reasoning, recognizing patterns, and making decisions. According to some, AI encompasses various subfields, including machine learning, natural language processing, computer vision, and robotics.
Analytics: Analytics involves the use of data, reporting/BI(Business Intelligence), statistical analysis, and quantitative methods to gain insights and make informed decisions. Analytics is focused on extracting meaning from data to understand patterns, trends, and relationships. Analytics can include descriptive analytics (summarizing data), diagnostic analytics (exploring causes of past events), forensic analytics (use of data to detect fraud, bias, and errors), predictive analytics (forecasting future outcomes), and prescriptive analytics (providing recommendations and optimal actions). Analytics is a mix of old-school and new-school these days as AI-driven business intelligence gains momentum. Some argue that analytics and data science are essentially synonyms.
Statistics: Statistics is a field that involves the collection, analysis, interpretation, presentation, and organization of data. It focuses on techniques for data collection, summarization, and inference to make informed decisions and draw conclusions. Statistics provides methods for hypothesis testing, estimating parameters, and quantifying uncertainty using probability theory and mathematical models.
Machine Learning: One definition of machine learning is that it focuses on developing algorithms and models that enable computers to learn from data and improve performance on specific tasks without explicit programming. It involves training models on data to make predictions, classify objects, or discover patterns. However, machine learning is so broad that it includes simple supervised models like linear regression and more complex algorithms such as neural network and Support Vector Machines (SVM), there can be considerable computer coding to prepare data, analyze it, and create models. Machine learning algorithms can be categorized into supervised learning, unsupervised learning, and reinforcement learning.
Econometrics: Econometrics is a branch of economics that applies statistical and mathematical methods to analyze economic data. It combines economic theory, mathematical modeling, and statistical techniques to estimate and test economic relationships, evaluate policy interventions, and make predictions in the field of economics. Econometrics often involves working with time series data and is typically based on economic theories and assumptions. Proponents of econometrics would argue that it includes everything data science does, but applied to economic, business, and finance problems.
There is so much overlap among these definitions, that distinguishing among them is quite subjective. New-school and old-school practitioners favor their own interpretations that may be based on education, industry, or the context of a particular problem. The glossary on this site goes into more detail.
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