Artificial Intelligence and Data Science sound very similar to each other. As a newbie it creates a confusion between these two terminology. But believe it, after reading this article your all confusion will come to an end. You will get to know the difference between these two.
Artificial Intelligence and Data science both are related to the Data but are different in the terms of usage. Where AI is purely focused on how to teach a machine to make some decisions, data science is a method of extracting meaningful information from that data.
Artificial Intelligence: A technique to embed intelligence in the Machine
Artificial Intelligence is a field of Computer Science which teaches a system beyond the calculation. Till now, our computer system where smart enough to just do calculations and perform the task they were instructed to do. But after the invention of Artificial Intelligence, we are teaching our devices task which only a human was specialized to perform.
Artificial Intelligence is a part of computer science where we teach a machine human like intelligence which gives the machine a ability of recognizing patterns and based on those patterns, they can take decisions and make predictions. Apart from the decision making and prediction making, AI is also capable of learning from the past mistakes.
There are various sub parts of AI which are Machine Learning, Natural Language Processing, Deep Learning, Data Science and many more.
Application of Artificial Intelligence
There are various applications of Artificial Intelligence but majorly it is used in Medical Industry, Finance industry, customer service, Educational purpose, and many more.
The main application that you can see in your surrounding are recommendation from shopping apps, OTT platforms, Chabot interacting with you, various translation tools, trending now a days AI generated Images, Audios and Videos.
Data Science: Extraction of insights from Data
Data Science is solely related to the Data. Data can be simply a word, image, video, audio or any thing. But data itself depicts nothing. When some meaning is extracted from the Data, that meaningful data is Information.
Data Science is a sub field of Artificial Intelligence which helps to recognize patterns, trends in the data using mathematical and statistical algorithms. Data can be in many form such as structured and unstructured. Getting insights and knowledge from that data is data science. This meaning information from the data science further helps in designing various machine learning, deep learning and NLP algorithms.
Applications of Data Science
Data Science is applied in Companies for data driven decision making and business intelligence. Apart from this, data science is used in designing of complex mathematical algorithms in Machine Learning which is further used in Artificial Intelligence.
Key Difference Between Artificial Intelligence and Data Science
Artificial Intelligence and Data Science are different as the Data Science is a sub field of Artificial Intelligence. AI uses Data Science to gain insights from the data which is further used in making decisions and predictions.
1. Difference on the basis of Definition
Artificial Intelligence and Data Science are different in their definition. AI is a broad concept which involves decision making, predictions, language translation, learning, pattern recognition, problem solving and many more.
Where as Data Science is a subpart which is used to analyze the data, gain insights from it, recognizing the patterns and trends in the Data, visualization of data.
2. Difference On the Basis of Scope
The scope of AI is in mimicking the human intelligence, making decisions, doing predictions. Where as the scope of Data Science is collection of data, cleaning of the structured and unstructured data, recognizing patterns, finding the trends that can be further used in decision making.
3. Difference on the basis of Career Options
The major difference in artificial intelligence and data science comes when we talk about the career options in these fields.
If you choose Artificial Intelligence as your career option then you have to learn machine learning, some part of data science and may be sometimes deep learning and NLP also. You can become AI Scientist, Data Scientist, Machine Learning engineer or AI Researcher.
If you decide to study Data science then you unlock your career as a Data Scientist, Data Analyst, and a Data Engineer.
4. Difference on the basis of Methodologies used
Artificial Intelligence and Data Science have a slight difference in their approach and methodologies because the outcome and end goal of both are different from each other. AI uses Machine Learning, NLP (Natural Language Processing), Deep Learning, Data Science, robotics where as Data Science involves statistical analysis, Data Visualization, Data cleaning, data collection, Data Mining etc.
Tools For Artificial Intelligence and Data Science
Tool as a beginner to use for artificial intelligence and data science are mostly the same, just the work of the two are different. But whether you decide to become an AI Researcher or a Data Scientist, you have to use these tools:
- Python: A beginner friendly programming language suitable for AI and Data Science.
- Jupyter Notebooks: An IDE for Python Programming Language and for coding purpose
- TensorFlow and PyTorch: Frameworks used in Deep Learning and Machine Learning.
- Scikit-learn: Toolkit by Python for data mining and data analysis.
- Tableau and Power BI: Popular and industry level tools used for Data visualization.
And if you want all these tools at the one place you can download Anaconda which is a platform just for a person who is about to begin his/her career in Data Science and AI. In Anaconda you get Jupyter Notebook, Pytorch, TensorFlow, R language, Scikit-Learn, Spyder and many more. And if you use some more tools you can easily do remote task and boost up your work efficiency.
In the next upcoming article we will be discussing about salary ranges and other aspects of AI and Data Science. We will get in depth knowledge about topics such as machine learning, NLP and deep learning.
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