TOP 5 Technologies you must learn in 2018

In this post i am sharing top 5 technologies you must learn in 2018 and these technologies will helpful to survive in IT industry next 5 years. Take a look one by one

1. Artificial Intelligence:

Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a software think intelligently, in the similar manner the intelligent humans think.

AI is accomplished by studying how human brain thinks, and how humans learn, decide, and work while trying to solve a problem, and then using the outcomes of this study as a basis of developing intelligent software and systems.The core problems of artificial intelligence include programming computers for certain traits such as:

Knowledge

Reasoning

Problem solving

Perception

Learning

Planning

Ability to manipulate and move objects

Machine learning is another core part of AI. Learning without any kind of supervision requires an ability to identify patterns in streams of inputs, whereas learning with adequate supervision involves classification and numerical regressions

Robotics is also a major field related to AI. Robots require intelligence to handle tasks such as object manipulation and navigation, along with sub-problems of localization, motion planning and mapping.

2. Big Data:

The process of storing and analysis data to make sense for the organization is called Big data. In simple terms,data which is very large in size and yet growing exponentially with time is called as Big data.The volume of data being made publicly available increases every year, too. Organizations no longer have to merely manage their own data; success in the future will be dictated to a large extent by their ability to extract value from other organizations’ data.

For any application that contains limited amount of data we normally use SQL/Oracle/ MySQL,but what in case of large applications like Facebook,Google,YouTube? This data is so large and complex that none of the traditional data management system is able to store and process it.

Facebook generates 500+ TB data per day as people upload various images,videos,posts etc..Similarly sending text/multimedia messages,updating Facebook/whatsapp status,comments etc..generates huge data.If we use traditional data processing applications(SQL/ORACLE/MySQL)to handle it, it will lead to loss of efficiency. So in order to handle exponential growth of data,data analysis becomes a required task. To overcome this problem,we use Big data. Big data includes both structured and unstructured data.

Structured Data means the data which can be stored and processed in table format is called as a structured data. it is very simple to enter, store and analyze.Example: RDBMS

Unstructured Data means the data with unknown form or structure is called as unstructured data. Example: Text files,images,videos,webpages,PDF files,PPT,social media data etc..

Semi structured Data means combination of both Structured and unstructured data.Example: XML data.

Traditional management systems and existing tools are facing difficulties to process such a big data.R is one of the main computing tool used in statistical education Research. It is also widely used for data analytics and numerical computing in scientific research.

This type of Big Data come from Social Media,E-Commerce,Share Market and Airplane etc..

3. Blockchain:

A blockchain is a digitized, decentralized, public ledger of all cryptocurrency transactions. Constantly growing as ‘completed’ blocks (the most recent transactions) are recorded and added to it in chronological order, it allows market participants to keep track of digital currency transactions without central recordkeeping. Each node (a computer connected to the network) gets a copy of the blockchain, which is downloaded automatically.

Originally developed as the accounting method for the virtual currency Bitcoin, blockchains – which use what's known as distributed ledger technology (DLT) – are appearing in a variety of commercial applications today. Currently, the technology is primarily used to verify transactions, within digital currencies though it is possible to digitize, code and insert practically any document into the blockchain. Doing so creates an indelible record that cannot be changed; furthermore, the record’s authenticity can be verified by the entire community using the blockchain instead of a single centralized authority.

4. Data Science:

 Data Science is the future of Artificial Intelligence. Therefore, it is very important to understand what is Data Science and how can it add value to your business.

This data is generated from different sources like financial logs, text files, multimedia forms, sensors, and instruments. Simple BI tools are not capable of processing this huge volume and variety of data. This is why we need more complex and advanced analytical tools and algorithms for processing, analyzing and drawing meaningful insights out of it.

Why Data Science is now such a Hot career now?

 What are some examples of data science?

Google.

They are the definition of data science. Everything they do is data driven from their search engine (google.com), through their YouTube efforts, maximization of ad revenue, etc. Even their HR team is using the scientific method to evaluate strategies that make the employees feel better at work so they can be more productive. Google is not the best place to work just by chance.

Amazon.

Each product recommendation that you get comes from Amazon’s sophisticated data science algorithms. Actually, Amazon has implemented an algorithm that can predict with some certainty if you are going to buy a certain product. If the probability is high enough, they move it to the storage unit closest to you so when you actually purchase it, it could be delivered the same day.

Facebook.

Facebook is generating ad revenue like crazy since it has all that personal data for all its users. Since you interact with the platform, they know if you prefer cat videos or dog videos, so they know if you are a cat person or a dog person. They know what sports you are into, what food you prefer, the amount of money that you are willing to spend online. In this way, they can target their users in extraordinary ways, thus companies just love to use it as a medium.

That being said, not only huge companies have a data science division. Small businesses, blogs, local businesses,etc. use Google analytics for their needs and have seen huge gains from it. This is also a part of data science. You don’t need to be doing machine learning to monetize on data science.

Now, if your competitors are relying on data-driven decision making and you aren’t, they will surpass you and steal your market share. Therefore, you must either adapt and employ data science tools and techniques, or you will simply be forced out of business.

5. Cloud Computing:

Actually, Small as well as some large IT companies follows the traditional methods to provide the IT infrastructure. That means for any IT company, we need a Server Room that is the basic need of IT companies.

In that server room, there should be a database server, mail server, networking, firewalls, routers, modem, switches, QPS (Query Per Second means how much queries or load will be handled by the server) , configurable system, high net speed and the maintenance engineers.

To establish such IT infrastructure, we need to spend lots of money. To overcome all these problems and to reduce the IT infrastructure cost, Cloud Computing comes into existence.

The goal of cloud computing is to allow users to take benefit from all of these technologies, without the need for deep knowledge about or expertise with each one of them. The cloud aims to cut costs, and helps the users focus on their core business instead of being impeded by IT obstacles.The main enabling technology for cloud computing is virtualization. Virtualization software separates a physical computing device into one or more "virtual" devices, each of which can be easily used and managed to perform computing tasks. With operating system–level virtualization essentially creating a scalable system of multiple independent computing devices, idle computing resources can be allocated and used more efficiently. Virtualization provides the agility required to speed up IT operations, and reduces cost by increasing infrastructure utilization. Autonomic computing automates the process through which the user can provision resources on-demand. By minimizing user involvement, automation speeds up the process, reduces labor costs and reduces the possibility of human errors.