The fields of data science and artificial intelligence (AI) are both rapidly evolving and gaining prominence in today's technology-driven world. While they may seem similar to those unfamiliar with them, there are distinct differences between the two disciplines.
In this article, we will:
Explore the definitions of data science and artificial intelligence
Examine the differences between them
And discuss the relationship between data science, machine learning, and AI.
We will also touch upon the salary prospects in the fields of data science and AI.
Lastly, we will take a look at the AI and data science degrees offered at IU International University of Applied Sciences, and consider how they can help you start a career in one of the two disciplines.
So, data science and artificial intelligence - let's dive in!
What is Artificial intelligence?
Artificial intelligence is a branch of computer science, focused on the creation of intelligent machines capable of performing tasks that typically require human intelligence. It involves the development of computer algorithms that can analyze, understand, and interpret complex data, make predictions or decisions, and learn from experience.
AI can be further categorized into two types: weak AI and strong AI. Weak AI (also known as narrow AI) is designed to perform specific tasks, such as facial recognition or recommendation systems.
On the other hand, strong AI aims to develop machines that possess human-like intelligence and have the ability to think and reason.
Artificial intelligence has revolutionized various industries and sectors, including:
Healthcare
Finance
Transportation
Entertainment
Artificial intelligence is a rapidly evolving field that has the potential to transform various industries and sectors.
What is Data Science?
Data science, on the other hand, is an interdisciplinary field that combines scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.
It encompasses various techniques from mathematics, statistics, computer science, and domain knowledge to uncover patterns, trends, and correlations from data.
Data scientists use programming languages like Python or R, along with machine learning algorithms and statistical models, to analyze large datasets and derive useful information. Their work involves:
Data cleaning
Data visualization
And the development of predictive models to aid in decision-making.
To summarize, data science is a multidisciplinary field that combines scientific methods, programming skills, and statistical knowledge to extract insights from data.
Now, let's dive in to a comparison of data science vs artificial intelligence.
What is the difference between data science and artificial intelligence?
When it comes to the world of technology, two terms that often come up are data science and artificial intelligence (AI). While they both deal with data and involve machine learning techniques, they have different focuses and objectives. Let's take a closer look at what sets them apart.
Parameters | Data Science | Artificial Intelligence |
|---|---|---|
What it is | Collecting, analysing and producing findings from large sets of data. | Implementing a predictive model to assess future events or trends. |
What you need | Programming, logical thinking, and skills in data visualization tools, plus a sense of how to tell compelling stories. | Good skills in math, statistics, and programming, plus machine learning algorithms and deep learning familiarity. |
Goals | Finding secret patterns hidden in the data hay, and use them to make business decision gold. | Teach machines and computers to act in an intelligent, human-like way. |
Ultimate purpose | Help governments and companies make educated decisions based on the best information available. | Improve life by producing systems that assist people in their work and everyday needs. |
IU International University of Applied Sciences offers 50+ career-transforming Bachelor's, Master's and MBA degrees in IT, business, marketing, social & healthcare and psychology.
Data Science and Artificial Intelligence Key Differentiators
In our exploration of the differences between data science and artificial intelligence (AI), it's important to dive deeper into the intricacies of these fields. Let's continue our journey by examining the key elements that set them apart.
Types of Data
Data, which is essential in both data science skills and AI, comes in various forms. It can be sorted into structured, semi-structured, or unstructured data, each needing different handling. Both fields also work with standardized data presented in different ways, such as visuals, text, or math.
Scientific Processing
Scientific data processing is key in the world of data and AI experts. It involves a well-structured process with many complex steps. At its core, this often includes creating detailed models that can be used on different systems.
Techniques Used
Both data science and AI use different methods to gain insights and make informed decisions. In data science, this involves techniques like data mining, statistics, and tools for managing data. On the other hand, AI explores advanced methods like deep learning, machine learning, and natural language processing (NLP), which are applied in various areas.
Tools Used
Data scientists commonly use tools like SQL, Python, R, and PowerBI. These tools, while powerful, typically have fixed ways of working. In contrast, AI specialists use a broader range of tools designed to mimic human decision-making processes, often with an emphasis on automation.
Applications
Data science and AI have unique uses across different fields. Data science focuses on recognizing patterns and making predictions, especially in complex and extensive datasets, like in analyzing pandemics. On the other hand, artificial intelligence is used in areas like speech recognition, smart assistants, and assisted learning.
Models
In data science, models are primarily used to help humans make decisions by providing insights and predictions. These models act as tools for data-driven choices. In the realm of artificial intelligence, models have a different role. They are created to replicate and mimic human decision-making processes, often with a focus on automation and independence.
When to Use
The decision to use data science or AI depends on the specific needs and circumstances. Data science is most suitable when complex decisions must be made based on extensive data and numerous factors. Artificial intelligence, on the other hand, is versatile and can be applied to a wide range of tasks, from routine activities to complex problem-solving.
Examples
To better understand the difference, think of their real-world applications. Data science usually works behind the scenes, shaping the processes that power everyday products, even if it's not always visible to the end-user. In contrast, artificial intelligence drives products and services we interact with daily, like intelligent chatbots such as Chat-GPT and AI-powered search engines like Bard and Bing.
Taking the Next Step
If you're interested in becoming a data scientist, our Master's program in Data Science is the perfect place to start.
For those leaning more towards a career in applied artificial intelligence, a B.Sc. in applied artificial intelligence serves as the perfect starting point.
Read our guides on how to become a data scientist and on how to start an AI career, and choose the educational route that best aligns with your passion and goals in the data and AI realm.
Common Ground Between Data Science and Artificial Intelligence
In the realms of data science and artificial intelligence (AI), there exists a substantial common ground, which serves as a foundation for their integration. Both fields rely heavily on data as their lifeblood. Data scientists and AI practitioners harness data for tasks such as exploratory data analysis and data analytics to derive insights, whether it's for making informed decisions or developing intelligent systems. The synergy between data science and AI is particularly evident in the extensive use of machine learning techniques. Data scientists leverage machine learning algorithms to uncover patterns and trends within datasets, while AI specialists employ these same algorithms to enable machines to learn, adapt, and mimic human-like cognitive functions.
Furthermore, the shared objective of enhancing human life and optimizing decision-making unites data science and AI. Data science seeks to empower businesses and organizations with the information they need to make more informed choices, thereby improving their operations and strategies. Simultaneously, AI endeavors to create systems that can augment human capabilities, whether it's through chatbots, recommendation systems, or autonomous vehicles. This common mission underscores the symbiotic relationship between data science and AI, where one fuels the other, ultimately working together to drive technological advancements and innovations.
Kick-Start your future with IU's Data Science and Artificial Intelligence Degrees
If you're reading this, working in AI or data science might be one of your career goals. To reach this goal, you need to start by learning the right skills - and the best way to do that is by getting a relevant degree.
IU International University of Applied Sciences offers a range of attractive degree programs for individuals interested in pursuing a career in data science or AI. With their emphasis on practical knowledge and industry-relevant skills, IU's degrees prepare students for the challenges and opportunities in these rapidly evolving fields.
At IU, students can choose from several degree programs, including:
B.Sc. Applied Artificial intelligence
The perfect starting point for starting a career in AI: learn everything from programming with Python and statistics to machine learning, deep learning, and natural language processing (NLP), and work on AI projects from the model design phase to production.
Discover MoreM.Sc. Artificial Intelligence
Have a tech background, and looking to transition into AI? IU offers a Master's degree in artificial intelligence that you can complete in one year! Cover all of the basics of AI and deepen your tech skills - at the same time.
Discover MoreM.Sc. Data Science
This is your entry point to advanced positions in the data science field. Earn a Master's degree and learn how to engineering models and software for complex data applications.
Discover MoreMBA Big Data Management
This is your gateway to leadership roles in the field of big data. Earn an MBA in Big Data Management and develop the skills to analyze, manage, and strategically leverage data to drive business success.
Discover MoreWant to find out more? You can request your free information material at the top or bottom of this page.
Difference between Machine Learning and Data Science
Machine learning is a subfield of AI and a crucial part of data science. It involves the development of algorithms that allow computers to learn from data without being explicitly programmed.
In other words, it enables machines to automatically learn and improve from experience.
Data science, on the other hand, encompasses a broader set of techniques beyond machine learning. It includes:
Data preprocessing
Feature engineering
And data visualization, among others.
While machine learning is a vital component of data science, data scientists also utilize various statistical and computational methods to analyze data and extract insights.
Data Science vs Machine Learning vs AI
The terms data science, machine learning, and AI are often used interchangeably, but they are distinct entities with different scopes.
Data science is a field that encompasses various techniques and methods for extracting insights from data.
Machine learning is a subset of data science that focuses on enabling computers to learn and make predictions from data.
AI, on the other hand, is a broader field that aims to create intelligent systems capable of mimicking human intelligence.
While data science is concerned with handling and analyzing data, machine learning provides the algorithms and techniques to derive patterns and insights from data. AI goes beyond this by incorporating reasoning, problem-solving, and decision-making abilities, similar to those of humans.
Data science and Artificial Intelligence: Jobs and Salary
Both data science and AI offer promising career opportunities with competitive salaries. Data science professionals and AI professionals are highly sought after in many industries, including technology, finance, healthcare, and e-commerce.
The role you'll choose in each industry depends on your ambitions and career goals.
In data science, some of the key roles professionals work in are:
Data scientist
A data scientist is responsible for the collection, analysis, and visualization of data. A data scientist is also expected to build effective models for extracting the right information from the data they collect.
Depending on the company, a data scientist may work alone and report directly to the CEO, or they may work in a team and report to a team leader or head of business intelligence.
The salaries for data science professionals in this role in the U.S. range from $95,000 per year to $165,000, depending on region, industry, and years of experience.
Data engineer
A data engineer is a set of data science professionals whose work focuses more on the technical side of things. They're responsible for working with and extracting raw data, making sure it's accessible to decision-makers in the company, and that the data is collected and stored in an appropriate manner.
This role is often a promotion from an initial data scientist role, as it requires more experience.
The average salary for a data engineer in the U.S. is around $115,500 according to Glassdoor.
Data analyst
A data analyst is expected to work mostly on data analysis. What does that mean? Well, data analysis requires the cleaning, interpreting, and visualizing data by using the correct set of data visualization tools. The role is similar to that of a data scientist, but the professional focus is slightly different.
A data analyst in the U.S. typically earns around $62,500 per year - but that also fluctuates, depending on experience and sector.
Big data engineer
Big data engineers, like data engineers, work on extracting raw data - the difference being the amounts and volume of the data sets they work with.
Big data engineers can expect to earn an average salary of close to $162,000, but becoming a big data engineer usually requires several years of experience and well-rounded technical expertise.
In comparison, AI professionals typically work as:
Machine learning engineer
Machine learning engineers are responsible for creating and improving the tools used to train machines in machine learning. They're responsible for the more technical aspects of artificial intelligence.
In the U.S., a machine learning engineer earns an average yearly salary of $145,000.
Deep learning scientist
A deep learning scientist will usually work closely with a data scientist or machine learning engineer, in order to develop the algorithms used for artificial intelligence tools.
A deep learning scientist in the U.S. can earn an average salary of $130,000.
AI engineers
AI engineers do work that is similar to machine learning engineers, but they focus more on the end product, and less on the training and learning phase of setting up new tools.
They can expect an annual salary of around $131,500.
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