If you opt for studying a course/program on AI from any business college (MBA or BBA), more often than not, it’s paired with Data Science or Data Analytics or something else related to Data. Take MBA ESG, for example, one of the courses offered in MBA ESG is “Artificial Intelligence and Data Science.” This would beg the question in people’s minds – Why are AI and Data Science always mentioned together? How are they related? Let’s find out.
AI and Data Science are tightly coupled, interdependent fields, where Data Science provides the data-driven insights and foundation, while AI utilizes that information to create intelligent, automated, and predictive systems. AI and Data Science are like two entities in cahoots, working together, where one complements the other, and they perform efficiently to create magical results in a matter of seconds.
Let’s learn more about these fields and how they are related.
Understanding AI and Data Science
AI and Data Science are the two pillars of modern-day technology. They are interconnected but distinct from one another. Data Science studies a set of data provided, analyzes it, finds patterns, and then provides valuable insights. AI can then use that knowledge or insight to build intelligent, autonomous systems.
AI is about utilizing data to emulate human intelligence, whereas Data Science is about visualizing data and finding a set pattern to study and fetch valuable information.
Both these technological fields are highly interdependent, but function individually. Despite their dependence on each other, both AI and Data Science could possibly work on their own. One doesn’t compulsively need the assistance of another to function. However, this connection provides the right balance for the user to search for results and get the best possible information.
AI boosts data analysis and data science, providing the foundational data for training models.
Core Differences Between AI & Data Science
The table here highlights the core differences between AI and Data Science
| Aspects | Data Science | Artificial Intelligence (AI) |
|---|---|---|
| Core Idea | Fetching knowledge and insights from data. | Simulating human intelligence. |
| Main Goal | Analyze data to support decision-making. | Build systems that can think, learn, and decide. |
| Focus | Data Interpretation. | Intelligence Automation. |
| Scope | Data Cleaning, Data Analysis, Statistics, Predictive Modeling, and Visualization. | Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, and Robotics. |
| Tools & Technologies | Python, R, SQL, Power BI, Tableau, Pandas, NumPy. | TensorFlow, PyTorch, Keras, OpenCV, and NLP Libraries. |
| Career Outcomes | AI Expert, NLP Engineer, Computer Vision Engineer, Machine Learning Engineer. | Data Scientist, Data Analyst, Business Analyst, Data Engineer. |
How Data Science Powers AI Models?
Data Science is the technological field that analyzes a large set of data, finding patterns, studying its various inputs, and finally deriving some useful and insightful information from it. This is the primary step in a complicated process of solving a problem.
From there, AI picks up the relevant information derived from Data Science and then creates intelligent, automated, and predictive systems.
AI helps automate monotonous and repetitive tasks. It helps find trends and patterns in large datasets, making smart predictions based on past data.
Career Overlap Between AI & Data Science
Some of the career options that overlap between AI and Data Science are:
- Machine Learning Engineer
- Data Scientist
- Applied Scientist
Some other career options that nearly overlap between the two, but not quite, are as follows:
- AI Engineer – It’s similar to a Machine Learning engineer for Data Science
- Analytics Engineer – It is a career option associated with Data Science, but some AI professionals can also opt for it.
Skills & Tools Comparison
The skill requirements in AI are as follows:
- Strong Mathematical Skills
- Deep Learning
- Neural Networks
- Algorithm Designs
- Programming (Python, C++)
The skills required in Data Science are as follows:
- Statistics & Probability
- Data Wrangling and Cleaning
- Data Visualization
- Business Understanding
- SQL & Python/R
Tools that are required for AI are as follows:
- TensorFlow
- PyTorch
- Keras
- OpenCV
- Reinforcement Learning Frameworks
- NLP Libraries
Data Science
- Python
- R
- SQL
- Power BI
- Tableau
- Pandas
- NumPy
Which Career Path to Choose?
Since AI and Data Science are two fields that are very closely related and often go hand-in-hand, it wouldn’t make much of a difference if you choose either one of them. It will depend upon what field interests you more and what you are more passionate about doing. The job positions, career opportunities, salaries, and skill set required are also quite similar.
Frequently Asked Questions (FAQs)
1. Is AI part of data science?
No, AI is not part of Data Science. They are two interdependent and distinct technological fields in themselves. They are, however, very closely related to each other.
2. Can a Data Scientist Become an AI Engineer?
A Data Scientist can become a Machine Learning Engineer, which is very close to Artificial Engineer, a career that can be more closely pursued after studying a program in AI.
3. Which is Better: AI or Data Science?
There is no clear answer to that question. Depending on what you are passionate about, either Data Science or AI can be better for you. They have similarly lucrative salary options and career opportunities.
4. Do AI careers require Data Science knowledge?
While it’s not compulsory to have Data Science knowledge, one can attain knowledge about Data Science to enhance their AI job.
5. Are AI and Data Science taught together in MBA programs?
While not exclusively always, many MBA colleges, such as MBA ESG in Bangalore, do teach Data Science and AI as a singular MBA program.
