Data science and artificial intelligence are two of the most important technologies in today’s world. Although data science uses artificial intelligence in its operations, it does not fully represent artificial intelligence. In this article, we will understand the concept of data science vs artificial intelligence. In addition, we will discuss how researchers around the world are shaping modern artificial intelligence.
Data Science and Artificial intelligence
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Data science and artificial intelligence are most often used interchangeably. While data science can contribute to some aspects of AI, it doesn’t reflect all of that. Data science is the most popular field in the world today. However, true artificial intelligence is unattainable. While many consider modern data science to be artificial intelligence, this is not the case. So let’s take a look at Data Science vs. AI to clear up all your misunderstandings.
|Artificial Intelligence: What it is, how it works and examples
What is Data Science?
Data science is the current mainstream technology that has taken over industries around the world. Today, the world has experienced the fourth industrial revolution for Online Data Science Education. This is the result of the contribution of the massive data explosion and the growing need for industries to rely on data to create better products. We have become part of a data-driven society. Data has become an urgent need for industries that need data to make informed decisions.
Data science includes various core areas such as statistics, mathematics, and programming. Therefore, a data scientist must be proficient in them to understand trends and patterns in the data. This heavy skill requirement gives data science a steep learning curve. In addition, the data scientist must possess.
The various steps and procedures in data science include data mining, data manipulation, visualization, and data maintenance to predict the occurrence of future events. A Data Scientist must also be well versed in machine learning algorithms. These machine learning algorithms are artificial intelligence, which we will discuss in more detail in this article.
Industries need data scientists to help them make the necessary data-driven decisions. They help industries evaluate their performance and also suggest necessary changes to improve their performance. They also help the development team tailor products that customers love by analyzing their behavior.
What is artificial intelligence?
Artificial intelligence is the intelligence that machines have. It is modeled after the natural intelligence that animals and humans possess. Artificial intelligence uses algorithms to perform autonomous actions. These autonomous actions are similar to those that have been performed in the past and have been successful.
Many traditional AI algorithms had explicit goals, as was the case with pathfinding algorithms such as A*. However, modern AI algorithms such as deep learning understand patterns and find purpose in the data. Artificial intelligence also uses several software engineering principles to develop solutions to existing problems.
Recently, many major tech giants such as Google, Amazon, and Facebook have been using artificial intelligence to develop autonomous systems. The most famous example is Google’s AlphaGo. This offline Go game system defeated Ke Jie, the world’s number one professional AlphaGo player. AlphaGo used artificial neural networks modeled after human neurons that learn information and take actions over time.
How is artificial intelligence different from data science?
Let’s start learning about data science vs artificial intelligence with the help of the following points:
1. Limitations of modern AI
Artificial intelligence and data science can be used interchangeably. But there are certain differences between the two areas. The modern AI used in the world today is “artificial narrow intelligence”. With this form of intelligence , computer systems do not have the full autonomy and consciousness that humans do. Rather, they can only perform the tasks they are trained to do . For example, AlphaGo can beat the #1 world go champion, but he won’t know he’s playing AlphaGo’s game. That is, he has no consciousness.
2. Data science is complex
Data science is the analysis and study of data. A data scientist is responsible for making decisions that benefit companies . Moreover, the role of a data scientist varies by industry. In the day-to-day roles and responsibilities of a data scientist, the primary requirement is data pre-processing, that is, performing data cleansing and transformation. It then analyzes patterns in the data and uses visualization techniques to generate graphs that highlight analytical procedures. It then develops predictive models that find the likelihood of future events occurring.
3. Artificial intelligence is a tool for the Data Scientist
For a Data Scientist, artificial intelligence is a tool or procedure. This procedure is on top of other methodologies used for data analysis. This can best be compared to Maslow’s hierarchy, where each component of the pyramid represents a data operation performed by a data scientist.
The different roles and requirements of the company also highlight the key differences between artificial intelligence and data science. For example, several companies require purely AI-based positions such as Deep Learning Scientist, Machine Learning Engineer, NLP Scientist, etc. These requirements are mainly about developing products that live and breathe AI. Many of these roles require Data Science tools such as R and Python to perform various data operations, but also require advanced computer science knowledge.
A Data Scientist, on the other hand, helps a company and businesses make informed decisions based on data. The Data Scientist is responsible for extracting data using SQL and NoSQL queries, cleaning up various anomalies in the data, analyzing patterns in the data, and applying predictive models to get future insights. In addition, depending on the requirements, the Data Scientist also uses artificial intelligence tools such as deep learning algorithms that perform rigorous data classification and prediction.
Data Science and Artificial Intelligence – Key Difference
- Data science is a complex process that includes preprocessing, analysis, visualization, and prediction. On the other hand, AI is the implementation of a predictive model to predict future events.
- Data science includes various statistical methods while AI uses computer algorithms.
- There are many more tools used in data science than in AI. This is because data science involves multiple steps to analyze data and extract insights from it.
- Data science is the search for hidden patterns in data. AI is about giving autonomy to the data model.
- With Data Science, we build models that use statistical data. On the other hand, AI is designed to create models that mimic human cognition and understanding.
- Data science does not require a high degree of scientific processing compared to AI.
In this article, Data Science and Artificial Intelligence, we introduced two interchangeable terms. Artificial intelligence is a broad field that is still largely unexplored. Data science is a field that uses AI to make predictions but also focuses on transforming data for analysis and visualization. Therefore, in the end, we come to the conclusion that while data science is a job that does data analysis, artificial intelligence is a tool to create better products and give them autonomy. I hope you enjoyed our explanation of Data Science vs. Artificial Intelligence.