Data science Write For Us – Formulating questions in data science are essential to getting a head start on modeling; it is useful to consider what the different types of questions are. Much of the discussion that follows comes from this article.
If we understand the type of question we are asking, we are taking a fundamental step in ensuring that our interpretation of the results is correct. We list 6 major types of data science questions:
Table of Contents
Descriptive Data Science Questions
Descriptive data science questions seek to summarize a characteristic of a data set. However examples include determining the proportion of men, the average number of servings of fresh fruits and vegetables per day, or the frequency of viral illnesses in a data set collected from a group of people. There is no interpretation of the result itself since the result is a fact, an attribute of the data set we are working with.
Examples of questions of this nature would be: “How much?”, “How often?”, “What percentage?”, “At what time?”, “How much is it?
Questions in exploratory data science
Exploratory data science questions are those in which you analyze data. That means, to see if there are patterns, trends, or relationships between variables. Moreover, these types of analyses are also called “hypothesis-generating” analyses because, instead of testing a hypothesis as you would with an inferential, causal, or mechanistic question, you look for patterns that support the proposition of a theory. Moreover, if we had a general idea that diet was somehow related to viral diseases, we could explore this idea by examining the relationships between a variety of dietary factors and viral infections.
For example, we found in the exploratory analysis that people who ate a diet high in certain foods had fewer viral illnesses than those whose diet was not enriched with these foods, so we hypothesize that among adults, eating at least 5 daily servings of fresh fruit and vegetables is associated with fewer viral illnesses per year.
An example of these questions would be: “Do you feel that you have a good or bad relationship with food?” «What is the effect of social networks on the attention span of adolescents?».
Questions in inferential data science
Questions in inferential data science would be a restatement of our proposed hypothesis as a question. They would be answered by analyzing a different set of data, which in this example, is a representative sample of adults in Mexico. By examining this diverse set of data, we are determining whether the association we observed in our exploratory analysis holds in a diverse sample and whether it holds in a model that is representative of the adult population of Mexico, which would suggest that the association is applicable. to all adults in Mexico.
In other words, we will remain able to infer that our hypothesis is true, on average, for the adult population in Mexico, based on the analysis carried out on the representative sample.
Examples of these questions would be: “How did you come to that conclusion?” and “Why does salt keep ice from melting?”
Questions in predictive data science
Predictive data science questions intended to automatically predict the best possible answer choices based on the context of the question. However predictive enquiries most commonly used in quantitative research studies for a company. Moreover, predictive examinations can widely used in the model design phase.
Questions like this would be: “What are the business benefits?”, “What technical knowledge do I need?”, “How clear will the results be?”, “What about the follow-up questions?” And what about commercial users? “How accurate, complete, and consistent the analytical techniques?”, “Can we perform the incremental analysis?”, “How effective is the data management?”, “Can the analytical system be integrated with our existing systems?”, “What support will be available?”
Questions in causal data science
Although an inferential question might tell us that people who eat certain types of food tend to have fewer viral illnesses, the answer to this question does not tell us whether eating these foods causes a reduction in the number of viral diseases, which would be the case for a causal question.
Causal data science questions hypothesize that changing one factor will change another element in a population. Moreover, sometimes the underlying design of the data collection allows the question you are asking to be causal. Moreover, an example of this would be data collected in the context of a randomized trial, where people were randomly assigned to eat a diet high in fresh fruits and vegetables or a diet low in fresh fruits and vegetables. In other cases, even if our data is not from a randomized trial, we may take an analytical approach designed to answer a causal question.
Questions of this type would be: «What is the effect of exercise on heart rate?», «What is the effect of hand fatigue on reaction time?», «What are the most powerful vectors for disease transmission?”, “How does exercise affect the rate of carbon dioxide production? «, «How does the temperature influence the diffusion of the air freshener?», «How does the concentration of silver nitrate affect the formation of silver crystals?»
Questions in Mechanistic Data Science
Finally, none of the data science questions described so far will lead to an answer that tells us if diet really causes a reduction in the number of viral illnesses. how diet leads to a reduction in the number of viral diseases. Moreover, a question asking how a diet high in fresh fruits and vegetables leads to a reduction in the number of viral infections would be a mechanistic question.
Questions in mechanistic data science are more of the type of describing the how at each step of the process.
something extra
There are a couple of additional points about question types that are important. First, many data analyses answer multiple kinds of questions. That is to say for example, if a study aims to answer an inferential question, the descriptive and exploratory questions must also answered during the inferential question-answer process.
To continue our example of diet and viral illness, we would jump directly to a statistical model of the relationship between a diet rich in fresh fruits and vegetables and the number of viral diseases without determining the frequency of this type of diet and viral illness. And their relationship with each other in this sample.
A second point is that the type of question we ask is determined in part by the data available (unless you plan to conduct a study and collect the data needed to perform the analysis). For example, we might ask a causal question about diet and viral illnesses to find out whether a diet high in fresh fruits and vegetables causes a decrease in the number of viral diseases and the best type of data to answer this causal question. It is one in which people’s diets change from one that is rich in fresh fruits and vegetables to one that is not, or vice versa.
Suppose this type of data set does not exist. However, in that case, the best we can do is apply causal analysis methods to observational data or answer an inferential question about diet and viral diseases.
Likewise, You can submit your articles at contact@computerinfoblog.com
How to Submit Your Data Science Articles (Data science Write For Us)?
That is to say, To submit your article at www.Computer Info Blog.com, mail us at contact@computerinfoblog.com
Why Write for Computer Info Blog– Data science Write For Us
Data science Write For Us
That is to say, here at Computer Info Blog, we publish well-researched, informative, and unique articles. In addition, we also cover reports related to the following:
interdisciplinary
statistics,
scientific computing,
scientific methods,
algorithms
knowledge
unstructured data.
statistics,
data analysis,
informatics
computer science,
information science,
computer science,
information science,
Guidelines of the Article – Data science Write For Us
Search Terms Related to [Data science Write For Us]
data science central
“Programming” + write for us
artificial intelligence writes for us
data science central
Data science guest post opportunities
Write for us data science
Data science blog submission guidelines
Contributing to data science blogs
Data science content contributor
Data science guest blogging
“big data” + “write for us”
Data science writing opportunities
Guest author guidelines for data science
Data science content submission
Data science blog guest posts
Writing for data science websites
Data science guest authoring
Data science content contribution guidelines
Guest posting for data science platforms
Data science blogging opportunities