In  the modern digital world, terms like data science and artificial  intelligence are often used interchangeably—but they’re not the same  thing.

While both are branches of computer science, there are many  differences between the two. If you’re interested in pursuing a career  in the tech space, you may be looking at different aspects of data analysis to see which area interests you most.

In this article, we’re going to help you with your decision-making process. We’ll discuss the differences between data science and artificial intelligence. We’ll also discuss salaries in these fields, skills needed, how to start a career in big data or AI, and so much more.

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What's The Difference Between Data Science and Artificial Intelligence?

Here's the key difference between data science and artificial intelligence (AI): data science is a broad discipline that includes the study of AI. Artificial intelligence is just one niche area of data science.

That's the short answer. Read on to uncover more about data science, and the exciting subspecialty of AI.

What is Data Science?

In simple terms, data science is the process of extracting useful insights from unstructured data. It’s an interdisciplinary approach that merges various fields of computer science, statistics, and scientific processes and methods in order to draw conclusions from raw data points.

Data science is believed to have brought about a fourth industrial revolution, and it’s now at the heart of business decision-making. Companies have realized the enormous value in data processing and analysis.  

Businesses large and small are capitalizing on the value of data science every day. The more data a company has,  the better business insights they can generate.

Companies like Airbnb  use data science to process and analyze their customer-generated data to  predict customer behavior. This allows the company to address service  issues and develop new features, products and services to offer their customers. Even insurance companies and banks now extract contact information using data science methods.  

Data science involves steps and procedures like data extraction,  manipulation, visualization, and data maintenance.

A data scientist is  expected to have knowledge of many different concepts and technologies,  including machine learning algorithms and AI. If  you want to work with artificial intelligence in depth, you’ll pursue a role like that of an artificial intelligence engineer.

What is Artificial Intelligence?

Artificial intelligence, often referred to as AI, is a collection of complex computer algorithms that mimic human intelligence. Computers that are programmed with AI can "learn" as they go, becoming better at solving specific types of problems as they take in more data.

It also involves translations, understanding human speech, image  recognition, speech recognition, and the process of decision-making.

Artificial intelligence is a product of human creation, developed to enable computers to read, understand, and learn from data, which helps in the decision-making process. These decisions are based on inferences that are otherwise difficult for humans to catch.  

In  modern technology, artificial intelligence is divided into two general uses: general AI and applied AI.

General AI handles tasks like speaking,  translating, recognizing sounds and objects, and engaging in business  and social transactions.

Applied AI refers to sensory technologies like  autonomous vehicles, aka self-driving cars.  Self-driving cars rely on artificial intelligence and innovative memory. They use algorithms to understand patterns and designs.

Today, algorithm implementations have advanced so much that we can run them from  smartphones and laptops.  

Data Science vs Artificial Intelligence: A Detailed Explanation

Now that you understand how the two relate to each other, let’s take a closer look at how they differ.  

  1. The significant difference is that data science involves pre-processing analysis, prediction, and visualization. AI is the implementation of a predictive model to foresee events.  
  2. Data science is an umbrella term for statistical techniques, design techniques, and development methods. Artificial intelligence has to do with algorithm design, development,  efficiency, conversions, and the deployment of these designs and  products.
  3. Python and R are the tools used in data science, whereas TensorFlow, Kaffee, and scikit-learn are tools used in AI. Data  science is primarily concerned with making use of data analysis and  data analytics (where it uses past and present data to predict future data). Artificial Intelligence is concerned with machine learning.  
  4. Data science was developed to find hidden patterns and trends in data. The discipline aims to extract useful data, process it, make sense of it, and ultimately use it to make important decisions. On the other hand, artificial  intelligence is used to handle data autonomously, removing the human  from the entire task to work on its own.  
  5. By using data science, complex models can be built for extracting various facts, statistical techniques, and insights. On the other hand, artificial intelligence  is meant for building models that emulate cognition and human understanding to a certain level. By emulating cognition, the aim is to create self-sufficiency, meaning the machine would no longer require any human input.  

To sum up, here are a few guidelines.  

You’ll use data science when:  

You’ll use AI when:

Salaries for Data Scientist and Artificial Intelligence Engineers

The average salary of a data scientist is approximately $116,654 per year. Companies offering these generous salaries recognize the power of big data and are eager to use it to boost business decisions. Even starting salaries are looking increasingly attractive in this growing field. An entry-level data scientist can earn as much as $93,167 per year, while experienced data scientists earn as much as $142,131 per year.

Similarly, the average annual salary of an artificial intelligence engineer is well above $100,000. The average national salary in the U.S. is $164,769 per year, with an average low of $90,000 and a high of $304,500. As career opportunities for AI engineers rapidly expand, AI engineers’ salaries will continue to climb.

How to Become A Data Scientist

A strong foundation in math, physics, and computer science will put you  in a great position to pursue a role in data science, regardless of  whether you choose to specialize in artificial intelligence. A basic  knowledge of linear algebra and calculus, as well as probability and  statistics, is also highly beneficial. Programming is particularly  important in artificial intelligence, as the algorithms for machine learning are different from those used in traditional programming. For  these and many other reasons, AI engineers will continue to be in high  demand across many different industries, including tech, financial  services, government, and consulting.

If you’re interested in a career in data science or AI, Thinkful’s innovative, online courses offer a great starting point to launch into  the tech industry. You could land a data science job 5 months from now  after taking our full-time Data Science program, which combines an in-depth curriculum with personal mentorship and career coaching. Or, if you prefer a more flexible option, our part-time Data Science bootcamp will get you there at a pace that suits your lifestyle.

If you’re still not sure which direction you want to pursue in tech, we’ve got a lot of insightful videos and articles on different careers in the field. Keep doing your research until you find something that excites you. There are so many options in tech to choose from, and you’re bound to find the ideal fit for you.  

Which Tech Career is Right for You?

Ready to change your career and join the world’s next workforce? At Thinkful, we’ve got your back with various tech programs to get you equipped with in-demand skills.

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