Data Scientist

Because more and more sectors are depending on data-driven decision-making the field of Data Science in India is predicted to expand dramatically over the next five years. The need for qualified data scientists is expected to increase by 2028 driven by industries including finance, e-commerce, IT and healthcare.

Opportunities for Employment: India is set to emerge as a major centre for analytics worldwide. Data scientists, machine learning engineers and artificial intelligence specialists will continue to be in great demand.
Pay Trends: Experienced professionals make up to INR 25 LPA while entry-level workers make between INR 8 and 12 LPA
Industry Adoption: New fields like fintech driverless cars and healthcare analytics will expand prospects.
Maintaining relevance in this ever-evolving industry will need ongoing skill development in Python, R, AI and cloud technologies

Yes, graduates of Bioinformatics courses work as Data Scientists. Both of these disciplines require a similar set of talents, therefore Bioinformatics courses graduates with the above-mentioned skill set can work in Data Science roles in the industry if they can adapt their mindsets to the subject. 

Candidates are left with the majority of the Data Science skill set plus a few more if they take the domain-specific requirements out of the Bioinformatics skill set. Most likely, those who transition from bioinformatics to Data Science will need to adjust to their employer's data organisation and distribution environment.

People from Data Science should be able to function in Bioinformatics professions after gaining some domain-specific skills and knowledge if the argument of core skill similarity is valid. 

Data science

Data Science is a broad field of study about data systems and processes aimed at maintaining data sets and deriving meaning from them. Data scientists use tools, applications, principles, and algorithms to make sense of random data clusters. Since almost all kinds of organizations generate exponential amounts of data worldwide, monitoring and storing this data becomes difficult. Data Science focuses on data modeling and warehousing to track the ever-growing data set. The information extracted through data Science applications is used to guide business processes and reach organizational goals.

There may be overlaps in these domains now and then, but each of these three terms has unique uses. 

 A business analyst profile combines a little bit of both to help companies make data-driven decisions.  

Artificial intelligence 

AI, a rather hackneyed tech term used frequently in our popular culture – has come to be associated only with futuristic-looking robots and a machine-dominated world. However, in reality, Artificial Intelligence is far from that.

Simply put, artificial intelligence aims at enabling machines to execute reasoning by replicating human intelligence. Since the main objective of AI processes is to teach machines from experience, feeding the correct information and self-correction is crucial. AI experts rely on deep learning and natural language processing to help machines identify patterns and inferences.

Machine learning 

Machine Learning is a subsection of Artificial intelligence that devices mean by which systems can automatically learn and improve from experience. This particular wing of AI aims to equip machines with independent learning techniques so that they don’t have to be programmed. This is the difference between AI and Machine Learning.