With the enormous rise in innovations in the world of data science, we can categorically say that the era of a spreadsheet is winding down very fast (if not already over). Activities such as Google inquiry, scanning of passport, your online shopping records are all loaded with data that can be smartly collected, intelligently analyzed, and technically interpreted to inform strategic marketing decisions that can be monetized. In less than ten years, the processing speed, ability, and prowess of CPUs are expected to match or equal that of the human brain.

With the surge of computers with a quick and big data processing power, many CEOs, CTOs, and decision strategists of organizations are constantly probing into ways of initiating and bringing about innovations into their companies. When such companies want to launch a new brand or service, they search out for a very competent and seasoned data analyst from the best data science Bootcamps for insights on market trends, market demand, the target demographic, etc. With this, Artificial Intelligence is being adopted into different organizations at a very fast pace to correctly analyze the data so collected.

Let’s have a quick look at some statistical facts about the development of AI for a more efficient working environment for data scientists. According to IDC in 2018, the global spending on AI and cognitive technologies stood at about $19.1 billion, which is 54.2 percent more compared to the previous year, and this could skyrocket to $52.2 billion in the year 2021. AI skills are among the increasingly- growing skills on LinkedIn, and in just two years (2015 to 2017), and amazingly 190% rise has been recorded. When we talk about "AI skills," we're referring to the requisite skills which can be acquired from the best data science Bootcamp needed to build and function in artificial intelligence technologies.

These technologies include expertise in areas like neural networks, deep learning, machine learning, as well as actual "tools," such as Weka and Scikit-Learn.         

What is Data Science?

Data science is the science that studies the generalizable distillation of knowledge from data. Data science integrates varying elements and builds on techniques and theories from different fields, including signal processing, mathematics, data engineering, pattern recognition, and learning, visualization, uncertainty modeling, probability models, machine learning, statistical learning, computer programming, data warehousing, and high-performance computing. The integration of data with all these disciplines is with the sole aim of drawing meaningful conclusions from data collected through critical analysis.

Who is a Data Scientist?

Data scientists are trained professionals who solve complex or intricate data problems by employing deep expertise in some scientific discipline. Data science is not a monopolized form of profession, although it is expected that data analysts can work with various tools of mathematics, statistics, and computer science. Expertise in these fields is not necessarily required (maybe in only one or two of the mentioned disciplines), and this implies that data science is a team profession where the memberships are experts and proficient across all the fields of other disciplines.

What is Artificial Intelligence?

Artificial intelligence (AI) focuses on the process of making machines demonstrate or simulate information like the natural intelligence of the human brain function. AI system is a technological advancement that has empowered machines with the ability to demystify problems based on the imputed data. In the modern technological concept, artificial intelligence is divided into two important areas. The first of it is general AI, which is built on the concept that a system can handle functions like speech-making and interpretation, identification of objects and recognition of sounds, performing business or social transactions, etc. The other one has applied AI that is based on concepts like driverless cars.

Artificial Intelligence Expert Openings

Because the application of artificial intelligence doesn’t only help us to carry out multiple operations but also make our jobs fast with a very appreciable level of precision. These and other reasons have led to the fast-rising need for this technology in almost all fields and thus create job openings that far outnumbered the job seekers. Several jobs are related to AI, and these are data analysts, computational linguists, machine learning engineers, predictive modelers, CMT analytics managers, data scientists, computer vision engineer, and information strategy manager. The spread of AI in its application is daily growing in bounds and leaps, and the Tech companies are heavily injecting a lot of funds into it. According to a PwC report, it is estimated that artificial intelligence could add $15.7 trillion to the world’s economy by 2030 — and boost North America’s GDP by 14% that year. Perhaps the most compelling aspect of AI is its seemingly limitless applicability. There are already so many fields being impacted by ML and now AI, including Education, Finance, Oil sector, and more. Critical and very sensitive areas within the Healthcare sector have been resolved using the AI techniques, impacting everything from variations in the care effort reductions to the analysis of the medical scan.

Positive Impacts of Data Scientist to AI Technologies

IBM Watson

This is an AI technology that helps medical personnel to quickly assess vital information in a patient’s medical record to provide applicable evidence and explore the best possible treatment available. It takes in a patient’s medical records then analyzes it using the in-built database of a coordinated assemblage of 300+ journals, 200 textbooks, and 15+ pages of texts which provide doctors with overwhelming instant access to a wealth of information tailored toward the patient’s treatment regime.

Blueberry

This robot can perform a very much more enhanced comedy after subtitles from hundreds of thousands of movies were programmed into it by a data analyst.  Kory Mathewson, an artificial intelligence researcher at the University of Alberta, Edmonton, created an algorithm designed to riff with him onstage. He trained it to create lines of dialogue to be used in an improved performance by rewarding it when the dialogue makes sense and punishing it when it spits out gibberish. While Blueberry will not be put to intelligence test auditioning at The Second City anytime soon, this delightful robot does sometimes hit the right note with funny lines.

Conclusion

Data science and AI are two disciplines with an interwoven relationship when it comes to the collection, handling, and processing data collected to make an informed decision. When it comes to the role of a data scientist in the transformation of AI, it is very obvious that these are the only set of career experts who are well rooted in various courses to harness the potentials of AI as well the untapped aspects of AI. As for other parts of AI which are yet to be utilized for solving real-world problems, it expected that bits of progress from data scientist would in no distant time uncover the full potentials of AI.

About The Author

Associate Instructor

Owais Rashidi

Owais is an associate instructor at QuickStart having prior experience of doing projects in .Net, SQL Server, SSIS, Data warehousing and Business Intelligence. He has done Bs in Enterprise Resource Planning (ERP) which is a unique blend of both Software Engineering and Business Administration. And is also configuration and implementation of SAP core modules.