5 IN DEMAND SKILLS AND TECHNOLOGIES IN TODAY’S ERA

Simplicity is about subtracting the obvious and adding the meaningful.

 

Do you think that one-day robots could be a reality and not just science fiction films? Or you could be sitting in your living room and get transported to the rainforests of Africa by wearing VR headsets? Or maybe you could control your whole house with just one click on a remote? All these are not only possible but becoming commonplace with the advent of New technological  fields like artificial intelligence, virtual reality, the Internet of Things, etc.

 

These technologies have spiced up the tech world and changed how things work. Now IT professionals need to continuously learn new things and unlearn their old ideas if they want to remain relevant in this new world. This has become even more important in the aftermath of COVID-19 which has entirely changed the way companies work and demonstrated the need for innovation in technology. In this situation, there is an increasing demand for employees and innovators in new and popular fields with not enough supply. But what fields are these? Well, read on to find out!

 

  1. DATA SCIENCE

 

This Specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.

 

*Skills developed :

  • Git hub
  • Machine Learning
  • R Programming
  • Regression Analysis
  • Data Science
  • R studio
  • Data Analysis
  • Debugging
  • Data Manipulation
  • Regular Expression (REGEX)
  • Data Cleansing
  • Cluster Analysis

Data is useless unless it can be studied and analysed to extract out the meaning inside it. And that’s precisely what Data Science does! This is an interdisciplinary field related to Computer Science, Maths as well as Business Knowledge and it mainly deals with extracting actionable insights from the data. Data Science is very popular currently and it is even called the “Sexiest Job of the 21st Century” with around 11.5 million new jobs created by 2026. A Data Scientist utilizes this power of Data Science to design new algorithms for data modelling, create data visualizations and predictive models as well as perform custom analysis on the data according to the business needs. For this, the basic skills that a Data Scientist possesses are Data Mining, Python, SQL, Statistical Analysis, Data Visualization, etc. and they are well paid with a salary of $115K per year.

 

  1.  Blockchain

 

The technology at the heart of bitcoin and other virtual currencies, blockchain is an open, distributed ledger that can record transactions between two parties efficiently and in a verifiable and permanent way. The ledger itself can also be programmed to trigger transactions automatically.

 

Blockchain is a chain of blocks where these “blocks” constitute digital information that is connected using cryptography. These blocks contain a cryptographic hash function linking to the previous block, a timestamp, and the digital information in the block. Blockchain is the newest method in security because the blocks are created in such a way that it is very difficult to modify the data. This ensures security while also maintaining transparency as the data in the block is not hidden in any way. That is why Blockchain is becoming very popular in fields like banking, finance, etc. where security is a must! The global spending on Blockchain was 1.5 billion in 2018 and is predicted to grow to 15.9 billion by 2023. In fact, a Blockchain Engineer focuses exclusively on security using Blockchain technology and creates the system architecture and decentralized applications for Blockchain with an excellent salary at $100K per year. This technology first got popular because of its implementation in Bitcoin and so a Blockchain Engineer needs to be educated in different Blockchain technologies that are used in Bitcoin, Ripple, Etherium, etc

 

 

  1. Artificial Intelligence

 

Artificial intelligence, is intelligence demonstrated by machines, unlike

the natural intelligence displayed by humans and animals, which involves consciousness and emotionality. The distinction between the former and the latter categories is often revealed by the acronym chosen.

 

Humans have natural intelligence but what about machines? Artificial intelligence is the field that deals with creating artificial intelligence for machines that mimics the natural intelligence gifted to humans. This is partially achieved by creating artificial neural networks that mimic the neurons in the human brain. Artificial Intelligence has a variety of applications in image recognition, natural language processing, autonomous robotics, etc. That’s the reason this technology is so popular with the AI market projected to grow to a $190 billion industry by 2025. A particular job that deals with Artificial intelligence is the AI Architect. He is responsible for creating AI solutions for the client as well as creating the system architecture based on the AI frameworks. The basic skills required for an AI Architect are languages such as Python, R, etc., and different AI technologies such as Machine Learning, Deep Learning, Artificial Neural Networks, etc. This is a great option for AI with an excellent salary package of $200K per year.

 

 

 

 

 

 

 

 

4.Cloud Computing

 

Cloud computing is the on-demand availability of computer system resources, especially data storage (cloud storage) and computing power, without direct active management by the user. The term is generally used to describe data centers available to many users over the Internet.

Cloud computing is a perfect solution for smaller companies with lesser resources with 94% of companies already using a cloud computing service in one form or another. Companies can access different computing services like specialized software, databases, artificial intelligence, data analytics, servers, etc. over the internet, which is much cheaper than if they had to buy their own resources. These companies can run their applications on the best data centers in the world with minimal costs. And the popularity of cloud computing has given rise to the role of the Cloud Architect with a yearly salary of $150K. He is responsible for managing the cloud computing strategy of the company which includes cloud management, cloud application design, cloud architecture, cloud deployment models, etc. There are many cloud computing vendors that offer these services to companies such as Amazon Web Services, Google Cloud, Microsoft Azure, IBM Cloud, etc.

 

 

 

 

 

 

 

 

 

5. Machine Learning

 

 

 

 

 

 

 

 

 

Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

 

While Artificial Intelligence deals with creating an overarching method for holistic intelligence in machines, Machine Learning deals specifically with machines learning automatically without being explicitly programmed. So, Machine Learning is a subpart of Artificial Intelligence and much more widely used these days! The machine learning process starts with creating a machine learning algorithm and then training it by using training data before using it with actual data. The choice of the ML algorithm depends on the type of data and the kind of task to be automated. In the corporate world, a Machine Learning Engineer manages the machine learning models of the company. They also scale the theoretical ML models in real-life models that can handle terabytes of data. Machine Learning Engineer is one of the most in-demand jobs with a salary of $120K per year. There are many Machine Learning models in this world including Linear Regression, Logistic Regression, Regularization, Support Vector Machines, Decision Trees, Neural Networks, Unsupervised Learning, etc.