What’s the Scope for Data Science in 2020

Data science is the collaboration of all the various branches of science that are merged together like statistics, analysis, as well as machine learning and other related methods which are used as well to perform operations. Demanding new technologies are coming up right now in the world. Data Science is used to deal with the huge amounts of data coming through various sources into a company’s domain. Let’s go forward and discuss the future scope of data science.

What is data science?

Data science is the accumulation, storage and analyzation of data. Today it is one of the most important profiles in any technological organization.

Data science is everywhere around us. Starting right from the storage of bank account details to the storage of login /logout information on any website, data science has made it possible to store and work with such huge amounts of data without flinching. Data science can be found as an application everywhere you look at. For instance, all social sites online profile page pictures you interact with. They store the data of your personalized choices in the servers and use it to deliver a web page that is completely personalized for you and you only.

Not only social sites, but also online marketing sites utilize data science. They use data science to analyze your personal search pattern and store this data on the servers. They analyze it further and provide you with a personalized shopping offer. This is how online shopping sites like Flipkart, Amazon, eBay customizes and personalizes its page according to every user’s shopping pattern. You can check data science training in hyderabad.

But the task of data scientists does not start from the collection or the organization of data. It starts way before with the framing of the problem at hand. You cannot solve a problem without knowing what the problem is about. Thus, framing the problem is also a part of data science.

We have been discussing how a data scientist constantly collects and organizes data and analyses it further. We also wonder how it is different from that of a statistician’s work.

The answer is very simple, one of them works upon explaining and the other depends upon predicting.

Difference between a data analyst and data scientist

The main difference between a data analyst and a data scientist lies in the principle of their approach. A data analyst looks into a data sample and studies how a particular occurrence of an event has taken place in the past. This is among the responsibility of an analyst.

Although a data scientist also collects, organizes, and analyzes it, their profile asks a little more out of them. Data scientists are also expected to predict the next occurrence of an event. For this reason, they have to learn various algorithms and master machine learning architectures so that they can look into a particular occurrence of an event from various angles. They often have to figure out new perspectives by themselves. It might not be figured out by the data samples before either. One can say that a data scientist has to look into the future.

Let us look into the various types of data science out there:

• Predictive analytics (causal) – Suppose you are a bank and would like to predict whether some customer will make a payment on time or not. In this case, you need a predictive analysis. By using this, you can build a system that would make predictive analysis on someone’s payment history to figure out if he/she was on time about payments in the past.

• Prescriptive analytics (utilizing machine learning) – If you want a model that can not only predict what can happen in the future, but also suggest an effective solution or have decision-making power having the best interest of the organization in mind, this is your go-to algorithm. An example of this is Google’s new car which can drive itself. It can not only drive itself just by utilizing certain algorithms. It also takes created intelligence into consideration by examining its past experience while hitting the road.

• Making predictions using machine learning-

If you want to build a model for predicting the future trend of a certain system or organization, then this is what you exactly need. This works by using machine learning algorithms which can teach itself about how trends in the past will shape up the future. This falls under supervised learning.

• Pattern Discovery- Pattern Discovery is performed by using machine learning. This step is nothing but the preceding step before predictive analysis. You need this when you are not aware about the pattern of the data set. Pattern Discovery finds out the hidden patterns that are present in a particular data set. It analyses them and finds out the optimum solution.

Now we can very well distinguish Data analytics and data scientists. Data analyst course contains prediction and prescriptive analytics whereas data science course contains machine learning and predictive analytics.

The various ways in which data science is used are

Data science course is integrated into various technological concepts such as deep learning, Internet of Things, Artificial Intelligence etc. With the increase of the development in these fields, the impact of data science has increased drastically.

Who is a data scientist?

A data scientist is a person who has all the technical skills required to solve a complex issue. He/she does not just stop there, but keep on thinking about the various ways in which more problems can arise and think of a solution before they arise.

A big part of a data scientist is mathematics. They should be able to spot trends in data. In some ways, data scientists resemble excavating machines and they spend their time digging up information from piles of not-so-meaningful gibberish. This is the reason that data scientists are so much valuable. But not so long ago, data scientists were not that much popular. But suddenly the popularity of them has reached such great heights that have completely changed the way in which various companies work. 

What is the scope of data science in 2020?

The pace at which data science is developing is mind-boggling. Applications of data science, including artificial intelligence, deep learning, internet of things, are growing day by day.

There are various reasons why the importance of data scientists is increasing in such a rapid pace:

• Handling Data is Hard

The amount of data that every individual company has to handle is increasing gradually by multiple folds. Consequently, it’s becoming more and more difficult to handle the data. For instance, terabytes of data are transferred every day by various transactions. A data scientist is a person who can categorically collate and organize the data to make sense of it and gives the organization an idea of where the business is heading.

• New rules regarding data privacy

In May 2018, new rules regarding privacy and data regulations were passed. 2020 is set to have another such data privacy rule revision by California. What this comes to show us is that, people are concerned about their privacy and are pressurizing companies to safeguard their interest in data protection to take relevant steps. Thus, it has become extremely important today to be very careful with the user’s data. Organizations can’t be careless about it, especially when a number of data breaches and hacking attempts are going on every minute all around the world.

• Data Science is Developing

Data science is going through a period of fast development. Even though data science has progressed, as much as it has, it is still in its infancy. Chances with more specialized roles will emerge with time. There are multiple career choices today, which has a null growth. This surely isn’t one of them. One will always keep processing through the ranks and evolving in this career.

• Much friendlier virtual reality

Artificial intelligence’s use has been growing by leaps and bounds. In this age, Big Data has been flourishing in multiple fields such as deep learning and neural networking. We can find machine learning in each and every software nowadays. In the future, you could experience a machine and human interaction in every step.

• Blockchain

The technology, which incorporates cryptocurrencies suggests bitcoins as the blockchains. This is where the security factor comes in. Data scientists are expected to implement various data security and data handling procedures to support blockchains.

Thus, you can very well see that data science career is the need of the hour today. Let us check out the various options that you shall have as a data scientist

Careers in data science

Data scientists are so important that they are required in every technological field. Since this is a demanding career with a given hefty paycheck, it’s important that the individual is a highly skilled personnel and has higher educational capabilities still. The most common careers in data science include

• Data architect
• Statistician
• Machine learning expert
• Data analyst
• Infrastructure architect
• Application architect

There is so much need and a space for someone in every business today. It is expected to look at a dataset, analyze it and tell an organization how to utilize it to maximize their benefit.

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