In both cases, you're transforming the values of numeric variables so that the transformed data points have specific helpful properties. The difference is that: in scaling, you're changing the range of your data, while. in normalization, you're changing the shape of the distribution of your data.

: روابط التواصل مع المحاضر (المهندس حسن الحفنى / ماجستير علوم البيانات من كندا)
https://www.linkedin.com/in/hassan-elhefny-a64b78245
https://www.facebook.com/7assanElhefny/
https://wa.me/+201032066499
لحجز الكورسات من اكاديمية دليل الطالب :
https://wa.me/+201065761668
لتحميل المستندات :
https://drive.google.com/drive/folders/1AkhtyXjeJzZvzpRhwol999cCJpc7F6FD?usp=sharing

Data science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from noisy, structured, and unstructured data.
Data science also integrates domain knowledge from the underlying application domain (e.g., natural sciences, information technology, and medicine). Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession.

Data science is a "concept to unify statistics, data analysis, informatics, and their related methods" to "understand and analyze actual phenomena" with data. It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, information science, and domain knowledge. However, data science is different from computer science and information science. Turing Award winner Jim Gray imagined data science as a "fourth paradigm" of science (empirical, theoretical, computational, and now data-driven) and asserted that "everything about science is changing because of the impact of information technology" and the data deluge.

A data scientist is a professional who creates programming code and combines it with statistical knowledge to create insights from data.