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Data science can add value to any business who can use their data well. From statistics and insights across workflows and hiring new candidates, to helping senior staff make better-informed decisions, data science is valuable to any company in any industry. In this article, we are going to discuss Why is data science important for business?

 Data science

 Improving the relevance of your product
Data science methodologies can explore historicals, make comparisons to competition, analyze the market, and ultimately, make recommendations of when and where your product or services will sell best. This can help a company understand how their product helps others and, as needed, question existing business processes.
This constant analysis and reflection using data science provides a deep understanding of the market’s response to your company’s products and services. By taking a hard look at how your product is being used the most, you can rethink your model to ensure you’re offering the solutions that your customers need.

 Data science

Finding your target audience
It is estimated that we create roughly 2.5 billion GBs of data per day. With this ever-growing amount of data, collecting what’s important for your customers and your business can be a struggle. Every piece of data that your company collects from your customers – whether it be social media likes, website visits, or email surveys – contains data that can be analyzed to understand your customers more effectively.

 Data science

By using data science with the information your customer provides, you can combine data points to generate insights to target your audience more effectively. This means you can tailor services and products to particular groups. Finding correlations between age and income, for example, can help your company create new promotions or offers for groups that may not have been accessible before.

 Data science

Implementing data science methodology throughout your business can add value in a variety of ways across decision making, recruiting, training, marketing, and more. Data analysis can lead to making well-informed decisions that allow your company to grow in smart, strategic ways. Taking the time to use data science and discover the evidence behind your performance is a tool that every business should find valuable.

 Data science

Here are five common ways in which data scientists can add value to businesses:
1- Decision Making With Quantifiable, Data-Driven Evidence.
2- Testing Those Decisions.
3- Translating Your Data into Actionable Insights.
4- Recruiting the Right Talent for Your Organisation.
5- Identifying and Refining Target Audiences.

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 As the world entered the era of big data, the need for its storage also grew. It was the main challenge and concern for the enterprise industries until 2010. The main focus was on building a framework and solutions to store data. Now when Hadoop and other frameworks have successfully solved the problem of storage, the focus has shifted to the processing of this data. Data Science is the secret sauce here. All the ideas which you see in Hollywood sci-fi movies can actually turn into reality by Data Science. Data Science is the future of Artificial Intelligence. Therefore, it is very important to understand what is Data Science and How Does Data Science Work.

 Data Science

Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data. Data science is a “concept to unify statistics, data analysis and their related methods” in order 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, domain knowledge and information science. 

data 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.

 Data Science

How Does Data Science Work?
Data science involves a plethora of disciplines and expertise areas to produce a holistic, thorough and refined look into raw data. Data scientists must be skilled in everything from data engineering, math, statistics, advanced computing and visualizations to be able to effectively sift through muddled masses of information and communicate only the most vital bits that will help drive innovation and efficiency.

 Data Science

Data scientists also rely heavily on artificial intelligence, especially its subfields of machine learning and deep learning, to create models and make predictions using algorithms and other techniques.

click to know Why is data science important for business?

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A server is a computer that serves information to other computers. These computers, called clients, can connect to a server through either a local area network or a wide area network, such as the internet. A server doesn’t have the screen or keyboard. And although your computer stores files and data you’ve put on it, a server stores all the data associated with the websites that are hosted by it and shares that info with all computers and mobile devices (like yours) that need to access them.

 server

How do other computers connect to a server?
With a local network, the server connects to a router or switch that all other computers on the network use. Once connected to the network, other computers can access that server and its features. For example, with a web server, a user could connect to the server to view a website, search, and communicate with other users on the network.

 server

An Internet server works the same way as a local network server, but on a much larger scale. The server is assigned an IP address by InterNIC, or by web host. Usually, users connect to a server using its domain name, which is registered with a domain name registrar. When users connect to the domain name (such as “avalindata.com“), the name is automatically translated to the server’s IP address by a DNS resolver.

 server

The domain name makes it easier for users to connect to the server, because the name is easier to remember than an IP address. Also, domain names enable the server operator to change the IP address of the server without disrupting the way that users access the server. The domain name can always remain the same, even if the IP address changes.

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 A database management system (DBMS) is a software package designed to define, manipulate, retrieve and manage data in a database. A DBMS generally manipulates the data itself, the data format, field names, record structure and file structure. It also defines rules to validate and manipulate this data.

 database management system

Database management systems are set up on specific data handling concepts, as the practice of administrating a database evolves. The earliest databases only handled individual single pieces of specially formatted data. Today’s more evolved systems can handle different kinds of less formatted data and tie them together in more elaborate ways.

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 Database management systems

Connolly and Begg define database management system (DBMS) as a “software system that enables users to define, create, maintain and control access to the database” Examples of DBMS’s include MySQL, PostgreSQL, MSSQL, Oracle Database, and Microsoft Access.

 Database management systems

Why to Learn DBMS?
Traditionally, data was organized in file formats. DBMS was a new concept then, and all the research was done to make it overcome the deficiencies in traditional style of data management. A modern DBMS has the following characteristics
– Real-world entity − A modern DBMS is more realistic and uses real-world entities to design its architecture. It uses the behavior and attributes too. For example, a school database may use students as an entity and their age as an attribute.
– Relation-based tables − DBMS allows entities and relations among them to form tables. A user can understand the architecture of a database just by looking at the table names.

 Database management systems

Isolation of data and application − A database system is entirely different than its data. A database is an active entity, whereas data is said to be passive, on which the database works and organizes. DBMS also stores metadata, which is data about data, to ease its own process.
Less redundancy − DBMS follows the rules of normalization, which splits a relation when any of its attributes is having redundancy in values. Normalization is a mathematically rich and scientific process that reduces data redundancy.

 Database management systems

Consistency − Consistency is a state where every relation in a database remains consistent. There exist methods and techniques, which can detect attempt of leaving database in inconsistent state. A DBMS can provide greater consistency as compared to earlier forms of data storing applications like file-processing systems.

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 What is the Internet of Things?
The Internet of Things, or IoT, refers to the billions of physical devices around the world that are now connected to the internet, all collecting and sharing data. Thanks to the arrival of super-cheap computer chips and the ubiquity of wireless networks, it’s possible to turn anything, from something as small as a pill to something as big as an aeroplane, into a part of the IoT. Connecting up all these different objects and adding sensors to them adds a level of digital intelligence to devices that would be otherwise dumb, enabling them to communicate real-time data without involving a human being. The Internet of Things is making the fabric of the world around us more smarter and more responsive, merging the digital and physical universes.

 Internet of Things

How does the IoT work?
The basic elements of the IoT are devices that gather data. Broadly speaking, they are internet-connected devices, so they each have an IP address. They range in complexity from autonomous vehicles that haul products around factory floors to simple sensors that monitor the temperature in buildings. They also include personal devices like fitness trackers that monitor the number of steps individuals take each day. To make that data useful it needs to be collected, processed, filtered and analyzed, each of which can be handled in a variety of ways.

 IoT

Collecting the data is done by transmitting it from the devices to a gathering point. Moving the data can be done wirelessly using a range of technologies or on wired networks. The data can be sent over the internet to a data center or a cloud that has storage and compute power or the transfer can be staged, with intermediary devices aggregating the data before sending it along.

 IoT

Processing the data can take place in data centers or cloud, but sometimes that’s not an option. In the case of critical devices such as shutoffs in industrial settings, the delay of sending data from the device to a remote data center is too great. The round-trip time for sending data, processing it, analyzing it and returning instructions (close that valve before the pipes burst) can take too long. In such cases edge-computing can come into play, where a smart edge device can aggregate data, analyze it and fashion responses if necessary, all within relatively close physical distance, thereby reducing delay. Edge devices also have upstream connectivity for sending data to be further processed and stored.

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