There is a lot of buzz around data science and artificial intelligence

1. Scope: Artificial Intelligence is just limited to the implementation of Machine Learning algorithms; however Data Science incorporates several underlying operations of data.

2. Type of Data: AI contains the kind of data that are standardized as embeddings and vectors but, However Data Science will have various types of data like structured, semi-structured, and unstructured data.

3. Tools: The common tool used in AI are Mahout, Shogun, TensorFlow, Kaffe, PyTorch, Scikit-learn whereas the tools used in data science are SAS, R, Keras, SPSS, Python, etc

4. Applications: AI is applied in many sectors like the Healthcare, robotics, transport, automation, and manufacturing industries. Whereas Data science is used in the field of Search Engines like Google, Bing, Yahoo, Marketing, Banking, Advertising Field and more.

5. Process: In the process of AI, Future events are forecasted by making use of a predictive model. But Data Science involves a process of pre-processing, analysis, visualization, and prediction using data.

6. Techniques: AI will use algorithms in computers to solve complex problems, however, Data Science involves various methods of statistics.

7. Purpose: The main aim of AI is to automate the process and bring autonomy to the data model. But the main aim of Data Science is to find hidden data patterns.

8. Different Models: In AI, Models are expected to be similar to human understanding. But In Data Science, Models are developed to produce insights that are statistical in nature for the purpose of decision-making.

9. Degree of Scientific Processing: AI uses a very high degree of scientific processing when compared with Data science that utilizes less scientific processing.

So to sum up, you’ll use data science when:  

• Patterns and trends need to be identified

• Statistical insight is a requirement

• There’s a need for exploratory data analysis

• The situation calls for fast mathematical processing

• You need to use predictive analytics

And you’ll use AI when:

• Precision is required

• Fast decision-making is needed

• You require logical decision making without emotional interference

• Repetitive tasks are involved

• You need to perform a risk analysis