Data Science Vs. Artificial Intelligence (AI): Complementary or Competitive?

Data Science Vs Artificial Intelligence: Words often used interchangeably; the comparison calls for a thorough analysis

Data science and artificial intelligence are two terms that are used interchangeably because of certain common areas they operate in. It is particularly confusing for people who are aiming for data science and artificial intelligence jobs, to make a clear demarcation between the two disciplines. The fact remains that in spite of sharing common areas, they vary in scope, types of data, tools, and applications. While AI draws from Data Science to a large extent, it is not everything to AI. And data science is applied to a wide variety of fields while AI is still on its way to finding its footing in the market. Here is a primer on Data science VS artificial intelligence.

What is Data Science?

Data science involves extracting useful information from unstructured data. It is one of the fields wherein a combination of techniques lent from disciplines like computer science, statistics, and scientific processes are applied to raw data points to derive conclusions. A typical data science project involves steps like data extraction, manipulation, visualization, and data maintenance. However, the scope of the subject doesn't limit a data scientist's knowledge to the above-said areas. He is supposed to have knowledge of different concepts and technologies, including machine learning and AI.

What is Artificial Intelligence?

AI is the capability to make machines think like living beings, ie., humans and animals. In certain cases, intelligence can outdo humans too. Machines achieve this capability of performing autonomous actions through algorithms. Traditional artificial intelligence relies on learning from large datasets organized by data scientists or algorithms written for narrow applications having explicitly provided goals. But contemporary AI algorithms like deep learning understand the patterns and find the goal embedded in the data. Artificial intelligence not only spans machine learning but also perception functionalities that are otherwise not possible for machines. Functionalities like speech recognition, natural language processing (NLP), and machine vision fall under this category. An artificial engineer programs a machine for functions like learning, reasoning, and self-correction.

The Key Differences

Scope:

 Data science is about processing the data to derive insights for writing algorithms. Artificial intelligence is limited to implementing machine learning algorithms

Type of data:  

While data science deals with structured, semi-structured, and unstructured data, artificial intelligence contains data in the form of vectors and embeddings.

Tools:

Artificial intelligence requires tools like Mahout, Shogun, TensorFlow, PyTorch, Kaffe, Scikit-Learn, etc. Data Science tool kits consist of Keras, SPSS, SAS, Python, R, etc.

Process:

Data science essentially involves the process of prediction, visualization, analysis, and pre-processing of data. But artificial intelligence involves a lot of high-level, complex processing for the capabilities of forecasting using predictive models and thinking like humans.

The difference in models:

While data science models are developed to produce statistically oriented insights applied in decision-making, artificial intelligence helps build models that are close to human intelligence and cognition.

Degree of scientific processing:

Artificial intelligence requires a very high degree of scientific processing compared to data science. A data science project goes through a data science pipeline involving steps from data ingestion to the communication of insights. Whereas artificial intelligence involves complex processes of feeding model objects to generate the desired output.

Skills required

The typical skills a data scientist should possess include logical reasoning, programming knowledge, database management skills, and good presentation skills. An artificial intelligence engineer is should have a good foundation in Mathematics, Statistics, in addition to having programming knowledge. They should have a good grasp of machine learning, and deep learning algorithms.

Applications:

Computer vision, speech recognition, and recommendation engines are typical applications of Data science while applications in artificial intelligence include pattern recognition, anomaly detection, classification, predictive modeling, and sentiment analysis.