What is Data Science and How it is Related to ASP .NET?
Until a few years ago, only a few of us had heard of data science. Today, more and more organizations are opening their doors to big data and unlocking its limitless opportunities by increasing the value of data scientist who knows how to tease actionable insights out of gigabyte of data.
As the time is growing, the importance of data processing and analysis is increasing the data is playing a key role in the big enterprises' management and deciding their degree of success and taking the business of organizations to a new level.
In the current digitally advanced time, modern businesses are aware of the importance of data. Enterprises of all sizes have begun to identify the value of their huge collections of data and the importance of utilizing them properly. As organizations start on their journey to collect their data, they usually begin by batch processing their big data assets. This means gathering and aggregating web log data, the user clicks from an application.
So, first try to understand what exactly data science is?
In the simplest words, we can say, it collects the data from different sources and transforms it into decision-making knowledge. It is a science that is being driven by data, by means of getting useful insights on the sets of data available, plotting the data visually and forecasting the future.
On one hand, data science involves intense knowledge of various tools and programming languages including Python, whereas, the most basic requirement is the knowledge of some basic mathematics. With proper practice and guidance, anyone can learn about data science and can gain proficiency by experimenting on various sets of data.
Data science has certain features such as:
- Ability to access
- Extract and load
- Make Predictions
How does a Data Scientist work?
In the industry, most of the data scientists are provided with advanced and training in statistics, math and computer science. They have got a vast experience which also moves forward to data visualization, data mining and information management. It is common for the two to have previous experience in infrastructure design cloud computing and data warehousing.
Here are some listed benefits of the data scientist in the business:
- Reducing the risk and fraud: Data scientist is trained to find out the data that stands out in some way. They create statistically, network, path and big date methodologies for predictive fraud models that can be used to create alerts that help in ensuring the response whenever some unusual data is recognised.
- Delivering relevant products: The advantage that data science offers is that the companies find the locations where they can sell their services. It helps in delivering the products at the right time and help the companies to fulfil the demand of the consumers.
- Personalized customer experiences: One of the most crucial advantages that data science holds is its ability towards the sales and marketing teams to understand their audience on a very granular level. With this updated knowledge, an organisation can create the best possible customer's experiences.
How data science is related to ASP.NET?
There are enough of a good reason to use ASP .NET when you are looking to design a webpage or web application. It gives high speed, multi-language support and is economical. Apps built using ASP .NET are much faster and efficient when compared with other languages.
.NET is represented in the data science community. There are a couple of several reasons floating around this fact. The main reason is that much academic research uses domain-specific languages such as R, whereas Microsoft concentrates on .NET for general purpose programming.
ASP .NET works with Internet Information Server to deliver the content in response to client requests. While processing the request, Asp.net application development gives access to all .NET classes, custom components and databases. As we know that web forms are the main building block of application development in ASP .NET, they provide flexibility by allowing controls to be used on a page as objects.
As a summary, it is rather a tricky question to choose the technology to build out data scientist careers, without a context. The decision needs to be based on the proper research, on the background, industry domain and prospects. The technologies will take new shape and grow, as they must to meet the demands that the growth and complexity of data that humans are creating.
According to Gartner research, by the year 2020 more than 40% of data science tasks will be automated. Data science can add maximum value to any business who can use their data in an efficient manner.
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