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AI, Featured, Job

AI or Data Science? Who Wins in 2021?

AI and Data Science are two of the most promising computer science branches. Both professions offer high-paying salaries and have similar skill requirements. As a result, the question that begs an answer:  Who is the winner? 

What is Data Science?

Data Science as a term started appearing after 2008. Companies understood a  need for experts well-versed in data organization and analysis. Research scholars devised the ability to comprehend massive amounts of data, along with processing, extraction, visualization, and transmission would be a crucial asset in the future. 

Skills required to become a data scientist

The tasks of a data scientist revolve around the compilation and analysis of both structured and unstructured data. The tasks require a highly specialized skill set. 

Data scientists must have proficiency in:

Mathematics and Statistics

A firm grasp of both mathematics and statistics is essential for a career in data science. Statistical concepts of maximum likelihood estimators, distributors, tests, in addition to calculus and linear algebra are vital for data science and machine learning algorithms.

Modeling and Analytics

Data is as useful as the persons running analytics and modeling over it. Through critical thinking and efficient interaction, a data science professional can produce useful insights and make accurate predictions on possible outcomes. 

Machine Learning

While ML is not an absolute must, familiarity with concepts such as decision trees, logistic regression, and others definitely helps in the long run.


Data scientists require a good command over programming languages like Python, R, and others to effortlessly shift between theoretical ideas and practical implementations.

Data Visualization

The process of breaking complex data into small easy-to-understand parts with the help of visual charts, graphs, and other aid is a must for any data scientist.

Also Read: HashCash Consultants Extends Data Visualization Expertise to Aid Global Retail Chain

Data Science in 2021

Data Scientist as a profession occupied the 2nd best position in a 2021 Glassdoor annual listing of US jobs. The median base salary is $113,736, with a job satisfaction rating of 4.1 out of 5. The US Bureau of Labor Statistics forecasts a 28% increase in data science job positions by 2026- an estimated 11.5 million jobs.

Job Roles in the Data Science realm include: 

Data Analyst

The job role of a data analyst includes visualization, munging, data processing, and last but not least- optimization. 

Data engineers

This job role includes creating and optimizing Big Data environments for data scientists to run algorithms on stable and optimized data.

Data Scientists

Data Scientists offer data-backed insights and solutions to challenges faced by the business.


A statistician offers insights from clusters of data while assisting in building new strategies for engineers to follow.

What is Artificial Intelligence?

Artificial Intelligence can be broadly classified as a computer science branch that involves making smart machines, which are able to carry out tasks that need human intelligence and supervision. It is only a matter of time before AI transforms all work environments on a global scale. 

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Skills required to become an AI Engineer

The required skills of an AI engineer include:

Communication and Problem-Solving Skills

Most aspects of AI are team efforts. AI engineers are assigned role-centric tasks and collaborate with others to form the final product. They also need a proper pitch to convince stakeholders. 


Coding is integral in AI model building and implementation. AI engineers must be well-versed in programming languages, such as Python, Java, C++, and R.

Linear Algebra, Probability and Statistics

There are different categories of AI models- Hidden Markov, Naive Bayes, Gaussian mixture, and more. Thorough knowledge of linear algebra, probability, and statistics is essential.

Apache Spark and other Big Data technologies

Big Data technologies such as Apache Spark, Hadoop, Cassandra, and others are regularly used by AI engineers. Large data volumes, often exceeding terabytes and petabytes are often streamed or produced in real-time. 

Knowledge of Machine-Learning Algorithms and Frameworks

ML algorithms (e.g. Linear Regression, KNN, Naive Beyes), deep-learning networks(convolutional neural, recurrent neural, or generative adversarial) are widely used in AI, along with frameworks. Examples of AI frameworks include PyTorch, TensorFlow, and Caffe. Understanding how these technologies work is helpful.

AI Outlook in 2021

Industries all over the world are already embracing AI- making it one of the most interesting professions to be associated with. Glassdoor reports the average annual salary for AI engineers in the US is $118,410. Job roles are also growing at an annual rate of 74%.

Popular work designations in the AI Sector include:


AI Developers are responsible for the software development of AI Robots, in close collaboration with engineers.


AI Architects build and maintain the AI architecture for integration with the client’s business system. 

Machine Learning Engineer

ML engineers are associated with the development of predictive models using ML algorithms, deep learning algorithms, frameworks, and huge amounts of data.

Business Intelligence Developer 

A BI developer is in charge of the development, deployment, and maintenance of business intelligence interfaces. Vast experience in software development, database, and data analysis is essential, with a preferable Masters in Computer Science. 

Also Read: Role of AI and ML in Customer Acquisition and Customer Retention in Banking

Who wins?

Actually, both are winners. Yes, AI and Data Science are closely related and often work in collaboration with one another. Data science offers insights and solutions to business challenges- based on consumer patterns. Their applications of Data Science are omnipresent- even outside of AI. Healthcare, transport logistics, e-commerce, and digital marketing, in particular, are heavily reliant on data science.

On the other hand, AI is catching up fast. It will probably take less than a decade for self-driven cars to be completely operational. Significant portions of manufacturing industries have been already automated. The speed of ongoing AI research indicates it won’t be long before AI becomes a useful tool in sectors like healthcare and education. In the media sector, news corporations like Bloomberg are using Cyborg AI technology for quick understanding of financial reports. The Associated Press used Automated Insights. Google is developing an AI to replace human calls for making appointments.

Also Read: AI-Blockchain Prototype for Energy Consumption and Distribution Patterns

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