As an established business owner, entrepreneur or professional, you have heard many new terms for technologies and domains of study, such as data science, AI and machine learning. You might ask how data science vs ai compares and what their role in your business and life is.
What is data science?
What is Artificial Intelligence (AI)?
Which one is better for my business?
Where do I begin?
In this post, we analyze data science vs AI, describing their fields of study, how they connect and what part each plays in helping you achieve the benefits of new tech.
The following are the areas covered in the post:
What Is Data Science?
Towards Data Science, a leading web that publishes articles and content about the field defines data science as:
Data science at its most basic level is defined as using data to obtain insights and information that provide some level of value …. An extension of the that definition would be that data science is a complex combination of skills such as programming, data visualization, command line tools, databases, statistics, machine learning and more… in order to analyze data and obtain insights, information, and value from vast amounts of data.
Data Science emerged from the necessity of making sense and extracting useful insights from the vast amounts of data that companies collect and store today.
Tech professionals are familiar with data analytics, as it has existed in the industry for some time. Data science takes data analytics to a whole new level by allowing organizations not just to describe what happened in the past but also to make predictions.
Everyday data science work involves four essential tasks:
- Data preprocessing, including cleaning and transformation.
- Pattern analysis.
- Data visualization.
- Prediction modeling.
Data Science is a broad interdisciplinary field. As a result, Data Scientists often needs to wear many hats:
- For data preprocessing, they need to know database engineering and design, SQL language, and other programming languages like Python. Also, they need to use essential tools like Excel and Microsoft Access.
- For pattern analysis and prediction modeling, data scientists need to be proficient at a combination of tools, applications, principles and algorithms to make sense of data clusters, including statistical analysis.
- For data visualization, they need working knowledge of tools like Tableau, Power BI, among others.
Data Science is about examining data and deriving patterns to assists organizations in looking at the big picture and making decisions.
What Is Artificial Intelligence (AI)?
Encyclopedia Britannica defines Artificial Intelligence (AI) as follows:
Artificial intelligence (AI), the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from experience….
Artificial Intelligence is not new. It has been around since the 1950s. However, practical applications were not possible due to the computers’ processing capabilities and limitations.
The exponential increase of computer processor capabilities has made practical AI possible, creating a new frontier that organizations are beginning to explore.
Teaching computers to play (and defeat) human players in games like Chess or Go are among AI’s most notorious applications.
Humanity has not yet created a general artificial intelligence to achieve human levels. However, narrow AI applications can perform particular tasks faster and more effectively than humans.
Artificial Intelligence Subfields
Since AI applications usually focus on specific problems, they have been subdivided into many subfields, for example:
- Computer Vision: Involves giving a computer the capability to process images and video and take some action. Among its applications are self-driving cars, medical X-ray analysis and diagnosis, face recognition and more.
- Speech Recognition: The ability of computers to recognize words and phrases in spoken language and convert them to a machine-readable format. It encompasses applications in voice recognition with digital assistants such as Google Voice, Alexa and Siri. Also, there is a wide array of apps in audio to text conversion and language translations.
- Machine Learning: The use of algorithms to understand data, learn and make decisions based on patterns hidden in data.
- Chatbots: Computer programs that use predefined scripts or natural language understanding to assist humans in various areas, including chatbots for ecommerce.
Machine Learning focuses on obtaining information from operational databases, determining the best model, and predicting future data. For example, predicting a given transaction might be fraudulent, determining the selling price most likely to succeed, and many more.
There are many other subfields of artificial intelligence. For instance, expert support systems that use predefined and elaborate rules to help humans make decisions are a form of AI. However, they need these rules to be rewritten (as opposed to learning them like machine learning apps). Also, pathfinding algorithms such as A* are a form of AI.
AI is about giving a machine the independence to perform a task or make small decisions faster and more effectively. In contrast, Data Science is about extracting meaningful insights from data.
Data Science vs Artificial Intelligence vs Machine Learning
A critical relationship and interaction between data science and AI happen in machine learning, which is a subset of AI itself.
Engineers build machine learning models from data, enormous quantities of it. In the development process, data science is the discipline responsible for extracting the data, analyzing it and selecting the model that makes better predictions.
Data Science uses statistical insights to find the best model. Afterward, engineers implement a Machine Learning solution to make future predictions and assist in decision-making.
Data science intervenes in several of the critical steps machine learning.
But the relationship between data science vs AI is reciprocal. While data scientists provide input for developing AI systems to satisfy business needs, they may also be users. Data scientists can use machine learning systems to help them make better predictions or provide more focused insights.
In that case, data scientists are both the designers and users of AI systems.
Not All AI and Machine Learning Solutions Have a Strong Focus on Data Science
Some machine learning solutions rely profoundly on data science, but not all of them.
For example, AI and machine learning systems trained with reinforcement learning obtain their data and determine the best behavior through direct environmental interaction. Effectively, they learn by themselves based on broad parameters defined by AI engineers.
Reinforcement learning is necessary when you lack enough data to solve the problem. In that scenario, data science cannot help you.
Engineers can use reinforcement learning to teach a robot to control its movement. The robot learns to walk and measure its leg movements using reward signals.
Another example could be, as discussed in a blog post in towards data science, allowing a traffic light control system to learn the best combinations to reduce traffic. In that case, the reward would be traffic reductions. The system would try different combinations, focusing on those that produce improvements.
AI solutions developed with reinforcement learning focus more on the engineering and operations research aspects and less on the problem’s data science aspects.
Is AI a Subset of Data Science?
AI is not a subset of data science but we can rely on it nonetheless.
Some AI systems integrate data science into their development processes, particularly those in the machine learning subfield’s supervised and unsupervised learning categories.
Moreover, data science can also use machine learning to help data scientists make predictions.
In a nutshell, both AI and Data Science can rely on each other, but none is a subset of the other.
Data Science vs Artificial Intelligence Which Is Better?
Are you starting a new business? Or are you looking for ways to grow an existing one? You know that you need to focus on new technology, but where to start?
You ask yourself: Data science vs ai, which one is better? Where should I put your resources?
Well, the answer depends on what you are trying to achieve.
Suppose you want to make sense of your data, obtain insights and make predictions to support decisions at the strategic and tactical levels. For example, you want to know which products fit a promotion better or make strategic product placements at your physical and online stores. In that case, data science is better for you.
On the other hand, artificial intelligence is better if you need to teach a machine to do specific tasks. With AI, you can train computers to identify fraudulent transactions, recommend products in real-time, or even walk.
However, if you pick one first, you will eventually need the other. For instance, a data scientist can use machine learning to add more value (a lot more). Also, when developing AI solutions that rely on data, you need data science to analyze and design models.
So the short answer is you will eventually need both of them.
Embrace Data Science and AI
In this post, we have examined data science vs AI and highlighted the disciplines of study encompassed by each. We have also discussed its similarities, contrasts and relationships with each other.
Also, we explained in which setting each one can help advance your business. You may go with data science if you seek insights into tactical and strategic decisions. On the other hand, if you need a machine to perform a given task, artificial intelligence is what you need.
We invite you now to take action. Reflect on how this new knowledge and technologies can help propel your business to the next level. Then, consider forming a data science or artificial intelligence department in your company and realize its benefits.
What do you think about how data science vs ai compares? Please leave a comment below.
