“What’s the difference between Artificial Intelligence and Data Science?” Many of you may ask.
In this modern world full of technology and machines, Data Science and Artificial Intelligence (AI) are the most widely used terms that are constantly evolving and gaining prominence. Both fields are revolutionizing almost every industry and shaping the future of how we interact with data and technology. But, not everyone understands the key differences between the two. Let’s do it for you.
What is the Difference Between Artificial Intelligence and Data Science Engineering?
| Parameters | Data Science | Artificial Intelligence |
|---|---|---|
| What is it about? | Identify and extract hidden insightful patterns from data for business. | Mimic human-like intelligence through algorithms. |
| Goals | Main aim of data science is to use data to make informed decisions and drive better business outcomes. | Main aim of AI is to automate processes and create machines that can perform tasks and make decisions intelligently and independently. |
| Types of data used | In data science, we can use different types of data like structured, semi-structured, and unstructured data for analysis. | For AI, we can use both structured and unstructured data. This can be in the form of vectors and embeddings for training and decision-making. |
| Tools used | A variety of tools are used in data science like Python, R, SQL, Apache Spark, Power BI, etc. | AI relatively uses fewer tools compared to Data Science like PyTorch, TensorFlow, Scikit Learn, Caffe, Keras, etc. |
| Techniques used | Data Analysis Statistical Analysis Machine Learning Data Visualization Regression Anomaly Detection Clustering, and more. | Machine Learning Natural Language Processing Supervised and Unsupervised Learning Deep Learning Data Mining Facial Recognition Reinforcement Learning Robotics, and more. |
| Output | Better insights, impactful patterns, trends, and future predictions. | Decision-making, problem-solving, automation. |
| Skillset | Proficiency in statistics, mathematics, programming, data visualization, data manipulation, SQL, problem-solving, effective communication, and machine learning | Strong programming skills, mathematics, algorithmic skills, critical thinking, problem-solving, and effective communication. |
| Domain Expertise Required | Data Science often requires domain-specific knowledge. | AI generally requires domain-specific knowledge |
| Applications | – Finance – Transport – Search Engine – E-Commerce – Healthcare – Image Recognition, etc | – Robotics – Healthcare – Chatbots – Voice Assistants – Digital Marketing – Gaming, etc |
| When to use? | – Fast mathematical calculation – Exploratory data analysis (EDA) – Predictive analytics for forecasting – Detect fraudulent activities – Identifying patterns and trends in complex datasets – Predict equipment failures – Risk assessment | – Minimse repetitive tasks – Perform risk assessment – Make fast decisions – Precision and accuracy is required – Predictive maintenance – Logical decision-making without emotional bias |
| Complexity | Complexity lies in data wrangling, feature engineering, model selection, and interpretability. | Complexity arises from intricate algorithms, handling uncertain environments, and ethical considerations. |
| Data Representation | Data science engineers often work with tabular data, time series, and textual data. | AI generally involves symbolic representation and often deals with vectors and tensors. |
| Human Involvement | Data Science often requires human intervention mainly for data analysis and making informed decisions. | AI aims to reduce human intervention by automating various tedious tasks. |
| Problem-solving Approach | It mainly focuses on solving data-related problems like prediction and analysis. | AI focuses on addressing large problems related to creating intelligent systems. |
| Security | – Main security concerns associated with Data Science include- – Data privacy and accuracy – Data cleansing problems – Handling of sensitive data – Generating fake data | – Main security concerns associated with Data Science include- – Attacks against AI models – Data poisoning – Data privacy concerns – Any misuse of AI – Risk with autonomous decision-making – Bias in data and algorithms |
Similarities Between Data Science and Artificial Intelligence
Now that you know the key differences between artificial intelligence and data science, let’s explore some of the similarities between them:
Data Usage
As we know, AI and Data Science field involve heavily working with data. Both use different tools and techniques to collect and analyze large sets of data that will help in deriving insights and making informed decisions. For example, in the healthcare sector, AI and data science are used to analyze patient data and detect any diseases, and improve treatment.
Predictive Analytics
AI and Data Science are also used to predict future outcomes on the basis of historical data. It requires the use of mathematics and statistics. For example, in finance, both fields are used to analyze current market trends and data, and then predict futuristic prices of stocks.
Machine Learning
Simply put, machine learning is the common link that connects both fields. Data science generally uses ML models to find insights from complex data sets and make predictions, whereas AI involves using ML algorithms that allow systems to learn from data and perform tasks independently. For example, when you use shopping apps, you receive a list of suggested products based on your past purchases. This process involves the use of machine learning to analyze your purchase history and behavior, and then suggest relevant products.
Problem-solving
Both AI and data science use various data-driven approaches like data analysis and data modeling to solve complex problems. For example, in agriculture, AI and data science is used to analyze soil conditions, check weather patterns, and go through historical data to increase crop yields and reduce usage of resources.
Salaries: Data Science Engineers vs Artificial Intelligence Engineers
With the increase in applications of data science and artificial intelligence today, the demand for data science engineers and artificial intelligence engineers has also increased and it is growing rapidly. Both AI and data science professionals are in high demand, and their future outlook is also very positive. As per the U.S. Bureau of labor statistics, employment for data scientists is projected to grow by 35 % until 2032, and the career growth of AI engineers is expected to be around 31.4% by 2030. Overall, both AI and data science engineers are the highest-paid professionals in the IT industry. Let’s confirm with numbers –
| Location | Average Data Science Enginners Salary | Average Artificial Intelligence Engineers Salary |
|---|---|---|
| India | ₹6L – ₹13L/yr | ₹6L – ₹12L/yr |
| US | $1L – $2L/yr | $1L – $2L/yr |
How to start your career in Data Science and Artificial Intelligence?
A career in data science and artificial intelligence looks promising, but the journey to becoming a data science and AI engineer can be both exciting and challenging. However, don’t worry! We have simplified this journey for you. Here are some tips you can follow to kickstart your career in Data Science and Artificial Intelligence easily:
Learn the basics
Start by building a strong foundation in computer science and mathematics. Also, master technical skills such as programming, machine learning algorithms, deep learning, and statistical analysis.
Gain relevant skills and get certified
Enrolling in online courses and earning recognized certifications is a great way to acquire relevant skills and showcase your expertise. The Scaler Data Science and Machine Learning course is an excellent choice.
Work on real-world projects
Begin by working on small projects to gain hands-on experience. You can also contribute to open-source projects or participate in online competitions. What matters is practice and exposure to real-world projects.
Build a strong portfolio
Now, it’s time to showcase your skills. So, create a strong professional portfolio highlighting your skills and projects in your desired field. This could be in the form of a PDF document, a website, or a GitHub repository.
Networking and Continuous Learning
The Data Science and AI field are rapidly growing, so staying updated with the latest technological advancements is crucial. You can network with other professionals in the field and join online communities to exchange knowledge and explore new topics and technologies.
Conclusion
Overall, Artificial Intelligence and Data Science fields are growing constantly with new technological innovations. These advancements are transforming industries and driving the demand for skilled professionals in both fields, thereby opening up a lot of job opportunities. The salary of Data Science and AI Engineers will keep rising as every industry increasingly turns to artificial intelligence and data science for driving growth and better decision-making. Also, jobs in the computer and IT field are projected to grow much faster than the average for all occupations from 2022 to 2032. So, what have you been waiting for?
FAQs
Which is Better: Data Science vs Artificial Intelligence?
Both Data science and AI fields can be considered as good career option with an abundance of opportunities. Which one to choose depends on your goals and interests.
Is Data Science a Good Career in India?
Yes, Data science is a good career choice in India. This is one of the in-demand professions that comes with stability, job satisfaction, and good salary packages.
Is AI ML a Good Career in India?
Yes, AIML is a good career choice in India because of its high demand and increasing applications across various industries.
Which Pays More AI or Data Science?
Both AI and data science professionals earn good salaries. But overall, AI engineers are paid more than data scientists.
What is the future of Data Science and AIML?
The future outlook of Data Science and AIML looks promising. All thanks to increasing technological advancements that drive demand for such skilled professionals.
