Chalmers in his book: What is this thing called Science? mentions that Science is Knowledge obtained from information. The most important endeavors of science are : Prediction and Explanation of Phenomenon. The emergence of Big (massive) Data leads us to the field of Data Science (DS) with the main focus on prediction. Indeed, data belong to a specific field of knowledge or science (physics, economy, ....).
If DS is able to realize prediction for the field of sociology (for example), to whom the merit is given: Data Scientist or Sociologist?
10.1007/s11229-022-03933-2
#DataScience #ArtificialIntelligence #Naturallanguageprocessing #DeepLearning #Machinelearning #Science #Datamining
It is interesting and simultaneously meaningless question. Data mining is more art than science but work in this area require much knowledge in mathematics. Mathematics is exactly science but data science - not. I have many publications in medicine, genetics,sociology, chemistry and biology but I am mathematician. Models which I help to develop can be used in different areas but I am the same. Data science is tool to use in different areas.
I am not sure that my explanation explain something but it is my understanding of your question.
Data science in its current state seeks to establish empirical statistical associations, data correlations, and patterns in the hope that they can help discover some hidden insights. But it is the reason why data science cannot be called real science at all.
In general, science can be broadly defined as a logical system of getting and organizing insights in some area of knowledge as a set of some principles that can be used for making some previously unknown but testable predictions. To qualify as a principle, insight must be both highly general (applicable to many settings) and stable (relevant now and in future developments).
What are specific data science principles that make it real science? Data science is just a loose collection of various computational methods applied to fit empirical data sets. An empirical association can, of course, be a starting point but it will never become real science until it includes a causal relationship between the variables (factors or features), i.e. understanding.
Indeed, is the data science forecast based on empirically collected data (the past) always possible? No, in general prediction or forecasting the future solely from the past data is not possible regardless of the amount of the past data. It is possible only in a limited number of cases in which a stable past data pattern can be reliably extended in the relatively short future horizon.
The data-centric thinking that dominates both data science and machine learning cultures is actually just 'data-fitting, in contrast to the “data-interpretation” that guides causal inference. The data-fitting school is driven by the faith that the secret to rational decisions lies in the data itself if only we are sufficiently clever at finding data patterns. In contrast, the data-interpreting school views data not as a sole object of inquiry but as an auxiliary means for interpreting reality, and “reality” stands for causal understanding of the processes that generate the data.
I think that is the difference between data analysis and data science. Data analysis concerns all technics machine learning and data science is the combination between the analysis expertise and the specific domain of knowledge. In this case, data science could be defined as a tool to produce knowledge and the research subject (e.g. nutrition, economics, transport) will be considered the science.
Evgeny Mirkes I am glad that we are both on the same page: data science in its current form is not science at all, it's just a loose collection of various statistical tools.
Yes, data science is considered a science because it involves systematic methods, processes, and algorithms to extract knowledge and insights from structured and unstructured data, grounded in principles of statistics, mathematics, and computer science.
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