Many organizations have accumulated large amounts of data thanks to rapid advances in data storage and collection. Because of large data sets, traditional analysis tools and techniques are not able to be used. Data Science combines traditional data analysis techniques with advanced algorithms to process large numbers of sets. Data Science has allowed for the discovery of new types and data.

Let's take a look at some of the most well-known data analysis applications.

Business: When we do business, we must be certain about the point of sale of our products to customers. Bar code scanners and smart cards technologies allow retailers to calculate the purchase history of customers at counters. This information is used by retailers, together with customer and business records, to better understand the needs of customers and improve their businesses.
Scientists in medicine, science, and engineering are quickly gaining data that could lead to new discoveries. Satellites in space provide data on what is happening in the world today, for example. The satellites provide data ranging from several terabytes up to petabytes. This is certainly a large amount.
Now that we have seen the basics of data science, let's move on to the more difficult applications.


Scalability: Data collection and generation are advancing at a rapid pace, with data sets ranging from gigabytes to terabytes or petabytes. We can create algorithms that can handle large amounts of data. This is called scalability. Scalability allows for easy access to individual records in a timely manner.
High Dimensionality: Today, it is common to handle sets that have hundreds or thousands of attributes. Bioinformatics uses ICU analysis to produce a large dimension of measurements as well as many features that allow for the tracking of human health. For some algorithms, the computational complexity of certain analysis algorithms increases with increasing dimensionality.
Complex and heterogeneous data: Traditional data analysis often deals only with sets that have the same attributes. Data is now heterogeneous, and more complex as data is becoming a booming industry.
Non-Traditional Analysis. Current data analysis tasks often require thousands of hypotheses to be valued. The desire to automate this process has motivated some of the techniques.
We can use attributes to help us classify the data as they are interrelated.


Differentiation: Equal or not equal
Order: <, >, <=, >=
Addition: + and-
Multiplication: * or /

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