Principles statistical and computational tools for reproducible science

What is the role of statistics in data science?
In conclusion, statistics plays a vital role in Data Science and Machine Learning.
It helps businesses and organizations to turn data into insights, enabling them to make informed decisions, optimize operations, and increase revenue..
Why is reproducibility important in data science?
Reproducibility is a cornerstone of the scientific method and ensures that tests and experiments can be reproduced by different teams using the same method..
In the context of data science, reproducibility means that everything needed to recreate the model and its results such as data, tools, libraries, frameworks, programming languages and operating systems, have been captured, so with little effort the identical results are produced regardless of how much time has passed

Learn skills and tools that support data science and reproducible research, to ensure you can trust your own research results, reproduce them yourself, and

Aspect of scientific research

Reproducibility, closely related to replicability and repeatability, is a major principle underpinning the scientific method.
For the findings of a study to be reproducible means that results obtained by an experiment or an observational study or in a statistical analysis of a data set should be achieved again with a high degree of reliability when the study is replicated.
There are different kinds of replication but typically replication studies involve different researchers using the same methodology.
Only after one or several such successful replications should a result be recognized as scientific knowledge.

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Principles statistical and computational tools for reproducible science

What is the role of statistics in data science?

Why is reproducibility important in data science?