AI & DS [Artificial Intelligence & Data Science]

"If Analytics is the engine, then Data is the 21st century fuel," says Simon Quinton. Without data, businesses would not be able to uncover useful insights that could help streamline their business. Without customer data, improved customer satisfaction or personal creation will not be possible.

If you are looking for Best tech Jobs in demand right now or future jobs you will get a list of job opportunities in industry stating:

·         AI architect

·         Business intelligence analyst

·         Cloud architect

·         Data (analyst, scientist, engineer)

·         Developer (web, software, mobile)

And if you look close into it you will get to know that in every field or industry there is a demand of Knowledge domain or skill set of Artificial Intelligence and Data Science.

Most artificial intelligence work positions in demand are those of machine learning developers, software engineers and data scientists. But people often get confused with these terminologies as they get additional details about DS or vice versa any time they search or talk about AI, so they either think or confuse that AI and DS are the same or come along with it, but here I'd like to notice key differences between the two domains or rather say ecosystems.

Artificial Intelligence Vs Data Science

1.   AI is implementing a predictive model for forecasting events to come. Data Science, on the other hand, is a systematic method involving pre-processing, analysis, visualization and prediction.

  1. AI uses computer algorithms while Data Science is composed of various statistical techniques.
  2. AI's about granting the data model autonomy while Data Science is about discovering hidden trends in the data.AI is for building models that emulate cognition and human understanding. On the other hand, with Data Science, we build models that use statistical insights.

So basically we will understand that AI is application oriented domain where DS is Analytics oriented where Analysis is done with help of various mathematical models. (Statistics, Probability and Linear Algebra, etc).

To better understanding simply get a look at Venn diagram for AI&DS



For insight into the diagram we had to learn the two interchangeably used terms. Artificial Intelligence is a broad, yet largely unexplored domain. Data Science is a discipline using AI to produce predictions while also focusing on data transformation for analysis and visualization. Hence, in the end, we conclude that while Data Science is a job that performs data analysis, Artificial Intelligence is a tool for making better goods and autonomously imparting them.

As it is required by the world.

Demand for skilled professionals in data science, including data scientists, ML, and AI Engineers, is on the rise. The supply of qualified professionals in the area, however, is creeping at a much slower pace. IBM predicts that by 2020, Data Science will take up 28 percent of all digital jobs, but unfortunately, due to a shortage of qualified candidates, job openings remain empty for as much as 45 days. IBM's The Quant Crunch Report also states:

“Machine learning, big data, and data science skills are the most challenging to recruit for, and can potentially create the greatest disruption if not filled."

With so many vacancies in Data Science, now is the time to upskill and take advantage of the golden opportunity!

Highly salaried jobs

Data Science is a highly advanced and exclusive field of study and there is no doubt that professional’s make big money in this area. For example, according to Pay Scale a data scientist's average salary in India is Rs 6,99,928, and a data analyst's average salary is Rs 4,04,924.

Many of the Data Science work positions have a relatively similar pay scale. The best part – you'll never have a boring career as Data Science is always developing. There will be plenty of opportunities for employment, upskilling and gaining more.

Application Areas

Data Science is a dynamic discipline that has found applications in all sectors, including healthcare services, finance, e-commerce, business and consulting. But only a handful of individuals have the necessary skill-set to make it big in Data Science. Also, job functions in data science also have overlapping skills, which imparts a degree of versatility and agility to Data Science professionals. There are plenty of vacancies to fill but there are not enough candidates to fill those roles.

1.   Data Scientist

By now, we are quite sure you would have understood the roles and responsibilities of data scientists. They are primarily involved in collecting data from multiple touchpoints, analysing it, interpreting it for inferences and insights and coming up with effective solutions for business concerns. Machine learning and artificial intelligence are integral parts of data science, where techniques from both such as regression, predictive analytics and more are applied for insight generation.

2.   Machine Learning Engineer

Machine learning engineers are some of the most in-demand workers in artificial intelligence, with an average income of over a Rs. 671,548. They are most sought-after by businesses, and are hired for operations immediately if considered necessary. Machine learning engineers come with software skills, natural language handling, statistics, applied math and working knowledge of tools like IntelliJ, Eclipse and more. If you're an AI aspirant, you might want to consider becoming a machine learning engineer for more.

3.   Research Scientist

A research scientist plays an interdisciplinary role. He or she will go back and forth between working on projects focused on artificial intelligence and machine-learning. Deep learning, reinforcement learning, natural language processing, machine vision, and more will require a research scientist. You need to have expertise in parallel computing, distributed computing, algorithms and computer engineering if you want to become a research scientist.

4.   Business Intelligence Developer

A creator of business intelligence brings business acumen to the table apart from the artificial intelligence and machine learning skills. He or she is responsible for crunching large amounts of market insights data, and working from a variety of viewpoints to increasing a company's income. Business intelligence engineers are responsible for holding company development on their shoulders, from developing and managing data for cloud-based applications to optimizing workflows and processes.

Apart from these, the potential for employment in artificial intelligence and machine learning also stretches through work roles such as


  • Full stack developer
  • Software architect
  • Data analyst
  • Data warehouse engineer
  • Product manager
  • Front-end developer

 


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