Data Science and AI: Exploring the Intersection of Two Revolutionary Technologies
Two of the most revolutionary technologies we’ve experienced in our lifetime are data science and artificial intelligence. From an academic point of view, you’d be hard-pressed to find two phenomena that have respectively changed the way we extract, analyze, compare, and use data for any given purpose.
Although separate, data science and AI have, do, and will intersect with one another frequently, and it’s important for us to understand how these two fields work together. Let’s explore the intersection of AI and data science and help you to understand how this intersection affects the way we learn and the way we think.
What is Data Science?
When we extract insights from raw data, there are four main ways of (and purposes for) doing so. More often than not, computational techniques are utilized in this process, offering more accuracy in the data itself, as well as the analysis thereof.
Let’s briefly describe the four main types of data analytics and their respective purposes.
Summarizing and presenting data for varying purposes is known as descriptive analytics. The main question being asked here is, “What occurred?” Past events have had some sort of an impact on the current status quo, and therefore it’s often important to analyse the cause of the effect.
Once this data has been analysed in a descriptive way, it can be utilized to encourage actionable decision making in a more meaningful way. Finance, sport, and marketing are key areas where descriptive analysis is frequently used.
When we analyze data for the purpose of finding the source of a particular problem, this is known as diagnostic analytics. The main question here is, “Why did it occur?”; an important question that leads to the root of any given problem that needs to be understood and ultimately solved.
In business and industry, regression can take place. Regression analysis looks into why it is taking place and how an institution arrived at its undesirable status quo. Hypothesis testing is another way to identify problems in diagnostic analytics because it asks several “What if?” questions throughout the analysis.
Predicting future events and their respective effects is an important research tactic known as predictive analytics. This is an interesting branch of data analysis, because it uses algorithms and statistical data to foresee future outcomes and manage things accordingly.
Predictive analytics is used very frequently in financial arenas, and assists professionals in that industry to manage risk, avert crises, and plan according to future outcomes.
Prescriptive analytics can usually be seen as a follow up to diagnostic analytics, in that we use this type of data analysis to prescribe a solution to any given problem. In short, well conducted prescriptive analysis will provide prudent recommendations for solving a problem, as well as actionable plans to avoid those problems in the future.
What is Machine Learning?
When it comes to data science, machine learning is an important component. Algorithms are developed from existing data to compute solutions, predictions, diagnostics and/or general analytics for a particular goal. Machine learning can be done in two ways:
- The algorithm is trained on labelled data and given in-and-output pairs with which to work;
- Or it is left alone as a standalone algorithm to detect patterns or structure within the data.
How Does Machine Learning Differ from Artificial Intelligence?
Machine learning is not excluded from AI. In fact, it’s a crucial component to AI in that it processes data, (or questions) using algorithmic methods. But AI also incorporates other methods such as language processing and computer vision (the ability to understand visual data from real world scenarios.
It’s interesting to note that AI is being used to develop applications that circumvent human involvement by implementing a type of ‘intelligence’. In a sense, Artificial Intelligence is ‘born’ right here. AI differs from machine learning in that it ultimately makes decisions based on an intelligent form of understanding or recognition.
How are AI and Data Science Intersecting with One Another?
The primary goal of AI and Data Science is the same: to analyze data, process that data, and come to a decision based on that analysis. As you can likely see, the two go hand in hand to ultimately serve the people who use them.
Data Science is the analytic process of the data itself, while AI is the consequential decision maker that acts on that data. AI is doing what would usually be done by humans, but automating the process and limiting the need for human involvement. Both Data Science and AI rely on Machine learning to grease the wheels of coherent automated decision making.
AI is the final piece in the puzzle for a vast majority of industries that once relied only on Data Science and Machine Learning. The leveraging capabilities of all three will become an essential component to accurate management, competing within respective industries, and increasing prime directives (such as profit, solving community problems, risk management, etc).
Not only does this simplify and streamline the lives of humans, but it also presents the possibility of coming up with solutions we never dreamed of. Solving the kinds of problems we’ve been grappling with for years may be on the horizon if the combination of Data Science and AI are properly harnessed.
In today’s data-driven world, the intersection between data science and artificial intelligence is becoming increasingly important. It’s not just about analyzing large amounts of data anymore—it’s about using that data to make informed decisions and predictions. The ‘birth’ of AI can help with this by using machine learning algorithms to analyze patterns in the data and make predictions about future outcomes.
The combination of data science and AI has the potential to change the way we live and work. From self-driving cars to personalized medicine, the possibilities are endless. As the field continues to evolve, it’s important that we keep asking questions and pushing the boundaries of what’s possible. How can we use AI to solve some of the world’s biggest challenges? How can we ensure that these technologies are being used ethically and responsibly?
These really are exciting times.