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Data Science vs Artificial Intelligence—what’s this “terminology skirmish” all about? While surely you’ve heard and probably even used both terms, how well are you informed about the meaning of each? This is a relevant question because many people believe them to be interchangeable and don’t feel much difference between the two. Yes, they can be closely connected, but Data Science and AI outline essentially different areas of expertise and work.

Let’s take a closer look at both terms and find out more about Data Science vs Artificial Intelligence. Get comfortable and read on!

What Is Data Science?

Understanding the difference between Data Science vs Artificial Intelligence

Before we take a deeper look at the Data Science vs Artificial Intelligence dispute, let’s begin with the basics. Data Science is a relatively broad area of study related to data systems and various processes focused on managing data. The ultimate result of employing Data Science solutions is the extraction of valuable insights from data that is either poorly structured or not structured at all. These are usually business-boosting insights much sought-after by all types of market players.

To do their work, data scientists combine specialized tools and techniques that enable efficient Big Data analysis. The goal is to elaborate models for tackling specific business tasks or issues. For this, specialists use mathematical analysis and identify data patterns and trends that can be put at the core of useful data models. 

Such models are then translated into comprehensive business predictions and recommendations. This requires quite an extensive set of skills, including strong math, statistical abilities, programming skills, and the ability to effectively communicate one’s findings. Knowing one’s way around various AI implementations is also an essential requirement.

Today’s companies generate huge clusters of data on a daily basis, and big chunks of those clusters can be true business-boosting gold. But massive data sets require data scientists to employ an extensive collection of tools and approach the analysis from different angles. The underlying tools may stretch from Python and Hadoop to SQL and the like, while the major approaches (apart from traditional in-depth business analytics and data visualization) are:

  • The prescriptive analysis. This kind of analysis aids firms in setting objectives by determining which activities are most likely to succeed. The prescriptive analysis draws on the model’s inferences and assists corporations in suggesting the optimum methods to meet their goals.
  • Predictive causal analytics: This approach is used to build business forecasts. It models and demonstrates the outcomes of different business actions and helps business owners to predict important future agendas for their companies.

When to use Data Science

Before we compare data science vs artificial intelligence, let’s see where Data Science can be used. Serving as a collective term outlining a range of statistical, design, and development techniques, Data Science can be applied:

  • When statistical insights are mandatory
  • When a user needs to identify trends and patterns
  • When exploratory data analysis is used
  • When predictive analytics need to be checked

Today, data science is widely used in such areas as healthcare, finance, human resources, government programs, and marketing. For instance, marketing teams use it to find out which product or service is more popular and is more likely to be sold. Targeted advertising is also pretty impossible without data science as its algorithms help to learn more about target users through personalized autonomous techniques. The modern healthcare industry simply can’t do without data science models as they help with drug development, image analysis, etc. 

What Is Artificial Intelligence?

Understanding the difference between Data Science vs Artificial Intelligence

Artificial Intelligence is an enormously widely-used term that can be mentioned across industries and niches. Often so, it is associated with smart robots or the future world where machines dominate many areas of human life and work. Such generalization is what sets it apart from the term of Data Science on a surface level. And going a bit more in-depth on the topic of artificial intelligence vs data science on the scale of the global IT market, more essential differences become ever so visible.

Artificial intelligence (AI) is a branch of computer science that deals with the creation of intelligent machines that work, think, and react to various circumstances like humans. AI research deals with the question of how to create computers that are capable of intelligent behavior, autonomous decision-making, and profitable work that machines may run themselves.

The key objective of artificial intelligence is to teach computerized machines from the user’s experience to perform different tasks based on this knowledge. All the experts from this niche count on comprehensive learning as this helps the machine to find patterns and inferences. As much as it can have an extensive field of application, the AI-powered industry is largely based on machine learning, natural language processing, and computer vision.

This spawns tools and software products built to pursue a single goal—help computers read and learn from data in real-time. The ultimate result is about making the decision-making process easier as well as much more informed and efficient in the long run. 

Today, AI products are most actively applied across industries, especially when it comes to retail and ecommerce, banking and financial services, healthcare and science, education, manufacturing and automobile production, and social media, among others. 

As for real-life applications, such well-known companies as Amazon, Google, and Facebook have long been using AI in their products. For example, almost all smartphone and tablet owners use artificial intelligence-based voice assistants such as Siri and Alexa for voice-based navigation within their devices. 

When choosing movies on Netflix, users enter their queries into a search that produces personalized results. People who contact a support service (for example, a bank) often deal with chatbots that gradually take the place of live call center employees. 

Elon Musk’s fans buy Teslas for the benefits of self-driving. Such examples can be listed almost endlessly, demonstrating how deeply artificial intelligence has taken root in the lives of ordinary people.

When to use Artificial Intelligence

Let’s cover the main cases where the active use of AI is most relevant:

  • When it is necessary to make quick decisions
  • When precision is required
  • When repetitive but complex tasks need to be automated
  • When risk analysis is required
  • When logical decision-making assistance is needed

Nowadays, AI is not an irreplaceable component of software development, but it is certainly recognized as a sign of advanced solutions and initiatives, which often pack functionalities that claim to be market-defining. It is commonly employed to provide users with personalized recommendations based on their online behavior (purchases, attention spans over certain digital components, website visiting habits, click-through rates, etc.).

Artificial intelligence is also increasingly used in healthcare to interpret and classify clinical notes, streamline paperwork management, and visualize complex medical assets. Natural language processing (NLP) systems may review unstructured clinical records on patients, providing impressive insights into healthcare service quality improvement, elaboration of treatment, and patient benefit.

Artificial Intelligence ultimately improves the speed, accuracy, and efficacy of human efforts by offering a range of automation, classification, and analytical opportunities. AI methods may be used in financial businesses to identify fraudulent transactions, develop fast and accurate credit scoring, and automate labor-intensive data management activities.

Data Science vs Artificial Intelligence: a Detailed Comparison Table

Now when you know a bit about both terms, it’s time to compare them and find the key differences and similarities. What is artificial intelligence vs data science? Have a look at the table below and see the main differences:

Artificial Intelligence

Data Science

The main goal of AI is to make a computer system act like humans to make smart decisions and solve complicated issues.

The goal of Data Science is to obtain insights from online data.

Artificial intelligence uses computer algorithms built for specific purposes.

Data Science is based on many commonly employed statistical techniques.

Uses machine learning-powered iterative processing and sophisticated algorithms to teach computers how to learn automatically.

Uses machine learning to source and process data to extract the valuable data for further analysis.

AI uses such tools as Keras, Tensor Flow, or Scikit Learn.

Data Science uses such tools as Tableau, MATLAB, or SAS.

Various chatbots and voice assistants are the most popular applications of AI.

Data Science is heavily focused on fraud detection or healthcare apps.

You should bear in mind that, despite the common artificial intelligence vs data science argument, AI is a must-have tool for data science. Simply put, it is used for analyzing data in a more autonomous, streamlined manner. The main goal of a data scientist is to extract data using NoSQL or SQL queries, solve all the issues related to data, analyze the patterns, and apply various models to generate insights. Plus, a data scientist uses AI tools to pursue one more important goal—to make rigorous data classification and prediction.

You can see that the field of data science encompasses a wide range of activities that include pre-processing, analysis, and prediction. On the other hand, AI is the application of a predictive model to forecast and outline future events.

Data Science vs Artificial Intelligence: Overview of Data Science and AI Specialists

Understanding the difference between Data Science vs Artificial Intelligence

In terms of comparing Data Science vs Artificial Intelligence, the former is a rapidly growing industry that offers an array of opportunities for both end-users and those engaged in this niche. Data scientists usually have their hands in IT and deal with math and computer science. One of the most exciting things about this industry is the massive collection of jobs available. The most requested Data Science job roles are as follows:

  • Data engineer
  • Database administrator
  • Data architect
  • Data scientist
  • Data analyst

Plus, you should bear in mind that this industry also offers many opportunities for students. They can work as Data Science interns, ML research interns, and Junior Data Science apprentices, and gain decent experience in this niche.

Now, let’s move to AI jobs. At this very moment, there’s a huge demand for various jobs in this niche. It’s an advanced technology that has already created many opportunities to deal with international tech companies and get a decent income. Here are the most-loved AI jobs that are in demand in 2022:

  • AI engineer
  • Business intelligence developer
  • Robotics engineer
  • Computer vision engineer
  • A natural language processing specialist

Conclusion

Overall, we have just covered the basics of AI and Data Science. You can see that both terms are related but different. You can see that in the dispute of Data Science vs Artificial Intelligence, the former mostly deals with computational calculations, usually performed on data. However, AI tools generate predictions based on data. In current times, both fields are in demand. If you are eager to get a good job in one of these fields, you should remember that if you are good with data analysis, then Data Science jobs might be right up your alley. If you like AI concepts, then the job in this area might suit your needs just perfectly.

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