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.
- AI
uses computer algorithms while Data Science is composed of various
statistical techniques.
- 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
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