Know Which Is Better: Data Science Or Artificial Intelligence

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Last Updated on: October 13, 2022

Data science and artificial intelligence are highly correlated. While the former is all about pre-processing prediction, analysis and visualisation, the latter is the implementation of predictive models to forecast events. India’s artificial intelligence market is expected to touch $7.8 billion by 2025, growing at a CAGR of 20.2%, according to The International Data Corporation report. Indian businesses are increasingly planning to invest in AI to address current business challenges in the fields of security, human resources and IT automation.

This explains why the demand for data science professionals in India is at an all-time high in 2022, while pay scales are rising and career growth opportunities abound. In fact, there has been significant growth in median salary in 2022 to ₹16.8 lakhs per annum, a 25.4% rise from the previous year. Today, AI is used in almost every industry, from automotive manufacturing to private and public banks, healthcare, power and steel, telecom and e-commerce.

GeekLurn, in partnership with NASSCOM and IBM, offers a comprehensive Data Science Architect Program to help career aspirants master the skills required to become data scientists. The program also offers deep insights into artificial intelligence with real-time applications to ensure job-readiness in this field. The course duration is 24 months, with 320+ hours of live training sessions, an opportunity to work on 50+ sponsored and funded research projects, 18 months of sponsored projects at authorised research centres and 6 months of live interactive classes. The format is online, which makes it all the more convenient. 

If you are wondering which is better: data science or artificial intelligence, this article will help you understand both concepts and decide for yourself.  

Table of Contents

What is Data Science?

Data science is an interdisciplinary approach to establish a problem, understand the business requirements and make the most of machine learning algorithms and data analysis to solve it. This is done by processing data extracted from multiple fields, such as computer science, scientific processes and statistics, to arrive at conclusions. Experts say that data science has brought in a 4th industrial revolution and is the core of major business decisions today.

This is because there has been a massive data explosion and industries are in dire need of data validation, governance, pre-processing and classification. This can help businesses create better products and customer experiences. The main goal of data science is to ask questions that can help locate potential study avenues. It is less focused on specific answers and more on a search for the right questions to ask, which the data can answer.

What is Artificial Intelligence (AI)?

AI is a “simulation of human intelligence” that can handle complex tasks like translating, speaking, engaging in business decisions, social transactions and recognising objects and sounds. Many giants, including Facebook, Google and Amazon, use AI to develop autonomous systems. AI systems ingest vast amounts of training data, analyse this data for patterns and correlations, and then use this analysis to make predictions. For instance, a chatbot is first provided with examples of text chats for it to learn how to produce lifelike interactions with people.

In short, AI helps in copying cognition and human understanding. It is required when repetitive tasks are involved, and fast decision making, precision and risk analysis are needed.

Data Science vs Artificial Intelligence – Skills Required And Job Roles:

Both data science and artificial intelligence are lucrative career opportunities, especially given their exponential growth rates. However, both areas are connected and not mutually exclusive. Skills that you require to pursue a career in data science include:

  • Programming skills in languages such as SQL, C, C++, Python, and R
  • Thorough knowledge of statistics and mathematics
  • Working knowledge of Machine Learning techniques
  • Reporting and data visualization skills
  • Knowledge of risk analysis
  • Familiarity with data structures and data warehousing

Whereas, the skills required for pursuing a career in artificial intelligence include:

  • Programming skills in languages such as C++, Python, or Java
  • Knowledge of probability and statistics
  • Knowledge of data evaluation and modeling
  • Understanding of distributed computing
  • Hand-on knowledge of using Machine Learning algorithms

As mentioned earlier, there’s an overlap of the skill requirements in both fields.  

Data Science Job Roles

Data science job roles include:

  • Data Scientist
  • Data Analyst
  • Data Architect
  • Data Engineer
  • Business Analyst
  • Statistician
  • Machine Learning Engineer
  • Database Administrator

Artificial Intelligence Job Roles

Just like data science, the field of artificial intelligence too has a varied range of job roles. They are:

  • AI Research Scientist
  • Machine Learning Engineer
  • Robotics Scientist
  • Big Data Engineer
  • Software Developer
  • Network Engineer
  • Business Intelligence Developer
  • Data Scientist

Difference between AI and Data Science

AI and data science are two of the most sought-after technologies that work with Big Data for effective decision making. Data science is mainly an umbrella term for design techniques, statistical techniques and developmental methods. AI is more about efficiency, conversions, algorithm design and deployment of these products and designs.

Below are a few key differences between data science and artificial intelligence:


Artificial Intelligence

Data Science

ScopeImplements machine learning algorithmsProcesses and analyses massive datasets for visualisation and analytics
Tools InvolvedScikit-Learn, PyTorch, Mahout, Caffe and ShogunSAS, Keras, Python, R and SPSS
ObservationImposes intelligence in machines using data to help them respond as humans doConsiders patterns in data to come up with well-informed decisions
GraphicUses an algorithm network node representationRepresents data in different graphical formats
ApplicationsAutomation, healthcare, robotics, manufacturing and transportationIdentifying patterns, statistical analysis, predictive analysis 

This data science vs artificial intelligence table is particularly important if you wish to take up a career in the tech space. Taking a look at it can help you figure out which area interests you more and then choose an educational program accordingly.

Data Science vs Artificial Intelligence – Salaries

The average salary of Data Scientists in India is ₹ 10.5 lakhs per annum. Depending on the years of experience, the salary ranges from ₹ 4.5 lakhs to ₹ 25.7 lakhs per annum.

On the other hand, the average salary of AI Engineers in India is ₹ 12 lakhs per annum. Depending on the years of experience, the salary ranges from ₹ 6 lakhs to ₹ 50 lakhs per annum. 

The bottom line:

AI is still in its nascent stages and experts are optimistic about its future applications. Data Science uses AI as a tool to generate predictions while focusing on data transformation for visualisation and analysis. So, learning more about data science vs artificial intelligence to know which is better will help you avoid using the terms inappropriately.


Is data science the same as artificial intelligence?

In this digital world, people often consider data science and artificial  intelligence as the same thing but they are not the same. Data science extracts useful insights from structured & unstructured data through models and algorithms. Artificial intelligence is the collection of complex computer algorithms that can copy human intelligence. Gadgets programmed with AI can learn on their own.

Is AI a good career option?

Artificial intelligence has a great career outlook. The Bureau of Labor Statistics is expecting a 31.4 percent increase in jobs related to AI. As per an estimate, an average salary of an AI engineer is around $125,000 a year in the U.S. AI is very versatile and can be put to use in a variety of fields; from medical to transportation, AI can be used everywhere, that’s why a career in AI is future-proof. 

How difficult is artificial intelligence?

Studying artificial intelligence can be a bit hard and most of the automation technologists feel they are not sufficiently prepared for the future challenges.Here are some reasons that make AI hard to learn: 
AI needs extensive programming – You have to learn coding in depth.
Data Proficiency- As machines need a lot of data, it can be a very difficult task to obtain and organise such data. 
AI is very complex – You need to learn computer science, statistics, calculus, and more to understand AI concepts.
Lack of AI tools- As developing new tools for AI is very difficult and time-consuming. All AI tools that we currently have are made from traditional programming. 

Neel is a Product Manager with an interest in Data Science, Machine Learning, Cloud Computing, DevOps, and Blockchain with expertise in Python, R, Java, Power BI and Data analytics.

Neel Neeraj

Neel is a Product Manager with an interest in Data Science, Machine Learning, Cloud Computing, DevOps, and Blockchain with expertise in Python, R, Java, Power BI and Data analytics.
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