Artificial intelligence vs Data Science

What is Data Science? 

Data Science is one of the highly booming technologies in the IT industry, which is now being utilized by every organization. As the organizations are involved in improving their services and operations, they are focussing on making the right decisions that will benefit the organization. The primary goal of Data Science is to provide meaningful insights by using the data systems and processes, deriving valuable information that helps in analysis.

Data Science includes different topics like statistics, mathematics, and programming. It is important to have knowledge of these topics to become a Data Science professional. In simple terms, Data Science refers to the collection of data to perform the data analysis and make decisions that benefit the organization.

Data Science is the framework that includes different phases in its lifecycle like data extraction, data manipulation, visualization, and maintenance. An individual who desires to become a data scientist should have sound knowledge of Artificial Intelligence and machine learning algorithms along with statistics, programming, etc.

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What is Artificial Intelligence?

Artificial Intelligence is the technology that is based on how machines can process based on human mimics. It is designed to perform simulation and imposition of human Intelligence into the machines. The actions are performed by the machines based on the events. 

Artificial Intelligence includes three steps called reasoning, learning, and self-correction. Most popular algorithms like deep learning and machine learning will help you gain an understanding of the patterns and trends for a given data which helps you analyze the primary goal.

In simple terms, Artificial Intelligence is a process that is based on human creation, which allows the computers to read, understand and learn from the data, allowing you to make precise decisions. Artificial Intelligence is categorized into two different types based on the general uses of general Artificial Intelligence and applied Artificial Intelligence. 

General AI: The general Artificial Intelligence is responsible for handling some of the activities like translating, speaking, and identifying the sounds and is involved in the engagement of social transactions and businesses. 

Applied AI: Applied Artificial Intelligence is responsible for performing the activities that include sensory techniques like self-driving cars, autonomous vehicles, etc., that depend on an innovative memory and Artificial Intelligence. These kinds of devices make use of algorithms to gain an understanding of the designs and patterns. Implementing the algorithms in mobile phones and laptops is an advanced level of usage in Artificial Intelligence.

Artificial Intelligence vs. Data Science: 

Now that you have got an idea of what Data Science and Artificial Intelligence are - which technology is better. Let us discuss some of the major differences between Data Science and Artificial Intelligence, which will help you gain knowledge of these technologies and also help you identify the best based on your skills and development.

1. Meaning: 

• Data Science: The primary goal of Data Science is to curate massive volumes of data to perform the visualization and analysis processes.
• Artificial Intelligence: Artificial Intelligence technology is utilized for the implementation of the knowledge and data of machines. 

2. Skills: 

• Data Science: To enhance your career in Data Science, you will need to have knowledge and expertise in utilizing statistical techniques for development and designing purposes. 
• Artificial Intelligence: When it comes to Artificial Intelligence, you will need to have knowledge of the usage of algorithms for designing and developing purposes. 

3. Technique: 

• Data Science: The Data Analytics technique is used in Data Science.
• Artificial Intelligence: The machine learning and deep learning techniques used in the Artificial Intelligence field. 

4. Observation: 

• Data Science: Data Science technology keeps looking for patterns in the data in order to make precise decisions. 
• Artificial Intelligence: Artificial Intelligence technology is involved in imposing the Intelligence on the machines using the available data to ensure that the machines respond in the same way as humans. 

5. Solving issues: 

• Data Science: The Data Science field makes use of a program or a part of a loop on how to solve the issues. 
• Artificial Intelligence: When it comes to Artificial Intelligence, it makes use of the loops and represents the loops for perception and planning purposes. 

6. Processing: 

• Data Science: Data Science makes use of a medium level of data processing to perform data manipulation 
• Artificial Intelligence: Artificial Intelligence makes use of a high level of data processing to perform data manipulation. 

7. Graphic: 

• Data Science: In Data Science, you are allowed to represent the data in different graphical formats.
• Artificial Intelligence: In Artificial Intelligence, you will need to make use of an algorithm that represents the network node presentation. 

8. Tools and technicals: 

• Data Science: The Data Science technology makes use of different tools like Keras, Python, R, SAS, SPSS, etc. 
• Artificial Intelligence: Artificial Intelligence technology makes use of tools like Caffe, mahout, shogun, TensorFlow, PyTorch, etc. 

9. Applications: 

• Data Science: The internet search engines like Google, Yahoo, and Bing make use of the applications of Data Science. 
• Artificial Intelligence: Applications of Artificial Intelligence are now being used in all kinds of industries like Health Care, transportation, automation and manufacturing industries, etc.

10. Use of Knowledge:

• Data Science: Statistical learning is used in Data Science to perform the analysis of data.
• Artificial Intelligence: Artificial Intelligence makes use of machine learning and deep learning.

11. Control:

• Data Science: Data control and manipulation take place with the Data Science technique.
• Artificial Intelligence: Artificial Intelligence makes use of machine learning techniques allowing to possess robotic control.

12. Goal:

• Data Science: The goal of Data Science is to research new algorithms, deploy and involve in automating the whole process.
• Artificial Intelligence: The primary goal is to deploy the models within a given application and include deep learning practices and neural networks.

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How does Data Science differ from Artificial Intelligence?

• Data collection: 

The primary goal of Data Science is to solve a particular problem. The data is collected by using statistics and advanced mathematics, which represents how the problem has arisen and what is the solution for the problem. When compared with Artificial Intelligence, Artificial Intelligence makes use of computer algorithms to predict the events that may occur in the future. AI always keeps collecting the data for analysis and keeps track of the trend changes. 

• Learning techniques: 

Machine learning is included in Artificial Intelligence, which helps in understanding and learning the data, and keeps initiating the predictive behaviors. Deep learning is based on the learning that copies or acts as per the human brain operations, which is also a subset of machine learning. With this, Artificial Intelligence helps in getting better results. When it comes to Data Science, Data Science makes use of data for analysis purposes. In this process, the data will be divided and organized, allowing the users to make the right decisions.

• Decision making: 

Artificial algorithms are used to create the events like humans act. In Artificial Intelligence, deep learning and machine learning are used to learn and predict behavior, allowing us to make predictive decisions based on the results. Chatbots on websites are the best example. In contrast to Data Science, Artificial Intelligence is capable of making its own decisions and acting as required.

• Application areas: 

In Data Science, there is a high opportunity to make better decisions by identifying the improvement areas or the areas that require modification. Data Science is now being used in multiple industries like healthcare, cybersecurity, etc. In contrast, Artificial Intelligence is capable of reading queries on the search engines like google, bing, yahoo, etc. AI holds the prediction behavior, which makes it popular in industries like robotics, eCommerce, IT, and many more.

Is Data Science required for Artificial Intelligence?

Artificial Intelligence comprises mathematical algorithms which allow the machines to gain understanding and help in analyzing the relations that exist between different data elements. The Artificial Intelligence engineer should have a basic idea of the fundamentals of Data Science and also knowledge of the programming concepts along with mathematics. 

The Artificial Intelligence engineer should be well versed in continuously learning and should be adaptable to changes that take place. They should also have an idea of the changing Data Science tools with hands-on expertise.

What are the Advantages and Disadvantages of Artificial Intelligence and Data Science?

Organizations are looking for innovative ways to ensure that business decisions are taken precisely. With the change in the fast computing power and improvement in the big data volumes, organizations have been focusing on innovative technologies like Artificial Intelligence and Data Science to meet their needs. 

Data Science is a vast field with its aim to derive meaningful output from the huge data sets by utilizing the principles and algorithms, while Artificial Intelligence aims to allow the machines to perform the execution by mimicking human Intelligence.

Let us discuss some of the benefits and limitations of Artificial Intelligence and Data Science. 

Advantages of Artificial Intelligence: 

1. Reduction in human error:

Human error usually refers to the mistakes that are made by humans from time to time. As you all know, computers will not make mistakes if the programming concept is working right. Artificial Intelligence is involved in gathering all the previous information and applying the algorithms to make precise decisions. With this, there is less chance of errors coming up, and accuracy levels will improve - thus leading to a reduction in human error. 

2. Take the risk instead of humans- Zero risks: 

Zero risks are considered one of the biggest advantages and a failure of Artificial Intelligence. With Artificial Intelligence, there is a high possibility of overcoming the limitations of humans by creating and developing a robot that is capable of performing risky things for us instead of humans. It could be anything that needs to be done; robots are capable of surviving and being resistant in nature, allowing them to work efficiently and appropriately as per the requirements. 

3. Available 24/7: 

A Human is expected to work for 4 to 6 hours per day. Humans will take the time to get refreshed and relaxed and will be starting a new day of work along with the weekends to balance their personal life and professional life. With Artificial Intelligence, robots are used and are expected to work for 24 hours on a daily basis without any requirement of breaks or will not get bored as well in contrast with humans. 

4. Assistance and repetitive activities: 

When we are into professional life, we will be busy working and performing some repetitive activities like dropping the thank you email or performing the verification of certain documents to identify the errors and many more like that. Humans are expected to do repetitive work. It is not the same in Artificial Intelligence. In Artificial Intelligence, the task can be automated.

5. Assistance through digital platforms: 

Most organizations are now utilizing digital assistants to establish interaction with their customers based on the qualities they have, which are also reducing the requirement of human resources. On the websites, you can make use of a chatbot, which will help you with all the information that you ask for. It is completely automated and is set up to answer a question with a particular answer. If the query changes, the answer will be updated as well. There are some chatbots that have become more innovative, which creates a doubt if you are chatting with a human or a chatbot. 

6. Faster decision-making: 

Humans are mixed up with emotions, while Artificial Intelligence is not. Artificial Intelligence helps in making unbiased decisions and also helps in making decisions faster than humans. 

7. Daily applications: 

Our day-to-day applications like Google's Ok Google feature and Apple's Siri feature are utilized by people to search for a particular location, for making phone calls, etc. 

8. New inventions: 

Artificial Intelligence is involved in discovering many inventions in almost every domain that is helping humans to clear and resolve most of the problems. 

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Disadvantages of Artificial Intelligence: 

1. High costs: 

Creating a machine to perform all the activities that a human can do is not an easy task. There is a lot of time and money involved in creating a machine that acts like human Intelligence. Artificial Intelligence is also involved in updating the hardware and software to meet their requirements dynamically, which includes high cost, thus marking it as a disadvantage. 

2. Making humans lazy: 

In Artificial Intelligence, we use machines or robots to perform all the tasks, whether it is repetitive or difficult. This is making humans become lazier and not allowing humans to memorize things, allowing humans to use their brains less, which is causing a problem for future generations.

3. Unemployment: 

Unemployment is one of the biggest disadvantages, which is making a lot of difference in terms of employment opportunities. In Artificial Intelligence, we are replacing humans with machines, and robots perform repetitive tasks. This is leading to a reduction or minimization in the number of job opportunities for humans as human interference is less.

4. Emotions: 

In Artificial Intelligence, we cannot expect emotions and a connection between the users and teams. It is true that machines work better than humans in a faster way but are not capable of establishing a connection, which does not allow them to develop a bonding with humans. 

5. Lacking out-of-the-box thinking: 

In Artificial Intelligence, the machines are set to work based on the programming or intended to perform the activities based on the coding that we do but do not have a tendency to think about new improvements or inventions. 

Advantages of Data Science: 

1. Demand and scope: 

There is a high demand and scope for the Data Science field, which provides numerous opportunities to job seekers. Data Science is also one of the fastest-growing technologies and profiles, which is expected to create around 11.5 million jobs by the end of the year 2026. 

2. Abundance of Positions: 

In the current times, we see that there are only a few people who have the skill set required to become a Data Science professional. As Data Science is one of the biggest booming fields, the number of opportunities is more. Due to the low number of data scientists available with the required skill set, the number of opportunities and the demand for Data Science is growing. 

3. Highly-paid career: 

If you are the one who is looking to enhance your career in the Data Science field, then you are making the right decision in opting for Data Science. It is one of the highly paid jobs, and the average salary of a data scientist is $116100 per annum. 

4. Versatility: 

There are many different applications for Data Science. It is being widely used in many different industries like banking, healthcare, e-commerce, consultancy Services, etc. As it is a versatile field, you will have the option to choose from many different industries. 

5. Data Science makes data better: 

The organizations are looking for data scientists who are skilled and have expertise in processing and analyzing the data. They are responsible for improving the quality and hence involved in enriching and improving the data to make it even better. 

6. Data Scientists are Highly Prestigious: 

The data scientists are the Data Science professionals helping the organizations to make precise decisions. The organizations are completely dependent on the data scientist and utilize their expertise to deliver better results to their customers or clients. Hence a data scientist is given the most significance in an organization. 

7. No boring tasks: 

The Data Science field has helped in automating the processes for multiple industries. Hence the organizations are now looking for the historical data and trying to train the machines to perform the repetitive tasks. This had made human jobs easier. 

8. Data Science Makes Products Smarter: 

The Data Science field has been involved in making the product smarter by utilizing the machine learning concept, which helps in creating better products to satisfy the customers and gain positive customer satisfaction. 

9. Data Science can save lives: 

The application of Data Science in the Healthcare sector is one of the crucial aspects which is allowing the healthcare departments to detect the early-stage tumors, allowing the healthcare industries to identify and cure the disease in the early stage itself. 

10. Data Science makes you a better person: 

In the Data Science field, you will not only gain professional experience and a great career but also have growth in your personal life too. You will have the capability to solve the problems and will be in sync with both your professional and personal life.

Disadvantages of Data Science: 

1. Data Science is called a blurry term: 

There is no clear definition for Data Science, and it is a very generic term that is used. It has become the first word that has made it tough to pen down about a data scientist. 

2. Mastering Data Science seems to be impossible: 

As Data Science is a mixture of many different fields like computer science, mathematics, and statistics, it is not possible to become a master or master all the concepts in all the fields. There are many online courses that have been available to improve your skill set in those feelings as well; however, you may not be proficient in all the skills. 

3. High level of domain knowledge is required: 

A person or an individual who has a background in Computer Science and statistics will find it difficult to solve a problem in Data Science without any knowledge of its background. 

4. Arbitrary data may yield unexpected results: 

A data scientist is responsible for analyzing the data and also predicting the future and making the right decisions. There are chances that it may not yield the expected results because of the utilization of the resources and weak management. 

5. Data Privacy problem: 

Data scientists are involved in dealing with the data; there are high chances that the data can be breached, and all the confidential information might get pirated. There is a high chance of data leakage happening, which affects the security aspects. 

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 Conclusion: 

After gaining an understanding of their advantages and disadvantages, along with the differences between Data Science and Artificial Intelligence, the Data Science field is a broad field that includes multiple advantages with its limitations as well. 

Based on the organization and the work environment, along with the client requirements, the organization needs to select the right field that helps in making the right decisions. I hope the above information is helpful to you. To get a clear understanding of these technologies, you can get trained and certified, which will make you a professional, paving the way for a brighter career. 

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