Artificial intelligence (AI) and data science are two of the hottest topics in the current tech world. They both seem to fall in the same area or are somehow connected. But what exactly are they? And how can we benefit from them to make our lives better? Do they have specific applications and meanings? Or are they just another facet of the trendiest technical domains?
All of these are reasonable questions.
Artificial intelligence (AI) means the capacity of computers to do tasks that traditionally have required human intellect, such as recognising and interpreting objects and natural language. On the other hand, data science is the study that extracts insights from large data sets.
Can we use these terms interchangeably as they refer to the slightly same concept of data? Yes and no! But before we talk about this. It’s much more important to make more detailed definitions of each category.
What is Artificial Intelligence (AI)?
There’s no question that AI is one of the significant drivers in both the digital age and society at large. It reflects the fourth stage of the industrial revolution. But what exactly is AI? It is the study of how computers mimic human intelligence in their actions and responses.
You might think it’s about robots who can clean our houses or even be our friends, just like Tom Hanks’s assistant Finch or these creations that would start a revolution against humans to take over the world.
Actually, no, it’s not! Indeed, manufacturing and developing robots are just a branch of AI. Artificial interference is also about allowing computers to learn and improve on their own by analysing data and making decisions based on that data.
DATA! It’s the magical secret behind this tremendous digital progress. All of these new concepts are fueled by data. And this is where data science comes in. Also, AI is divided into two major sections: general AI and narrow AI. General AI is artificial intelligence that can handle any task like a human being, no matter how complicated, like self-driving cars. Narrow AI is artificial intelligence designed to complete a specific task, like a chatbot.
One of the key differences between general AI and narrow AI is the ability to learn. General AI can take action in challenging situations. That means that it can adapt to new incidents, and then the machine can store this information to tackle similar problems in the future. Narrow AI, on the other hand, can only learn the specific task it was designed for.
Another key difference between general and narrow AI is the ability to work on any task. General AI is not limited to working on specific missions. It can work on any job that it is given, which requires a high level of abstraction.
What are Common Applications of Artificial Intelligence?
You can find potential applications for artificial intelligence (AI) everywhere. Some of the most common applications include:
- Automating repetitive tasks: AI can be used to automate repetitive and time-consuming tasks, such as data entry or analysis. Employees may thus have more time for imaginative or strategic endeavours.
- Improving decision-making: AI can help organisations make better decisions by providing insights that would otherwise be impossible. For example, AI has the potential to analyse massive amounts of data and spot trends and patterns that people would overlook.
- Enhancing customer service: AI can improve customer service by providing personalised recommendations and answers to frequently asked questions. Additionally, AI chatbots can handle simple customer service inquiries so that human agents can focus on addressing more significant issues.
- Increasing productivity: AI can increase employee productivity by automating tedious processes. For example, an organisation could use AI to automate finding and sorting data in project management software. That could allow employees to focus more on higher-level activities.
- Improving operational efficiency: AI can also be used to improve operational efficiency, for example, by analysing data to make automated decisions about the best course of action.
What is Data Science?
Understanding how to get knowledge from raw and unorganised data is the core of data science. It’s a new discipline that merges the study of numbers with computing and the business world. As a result, data scientists have a comprehensive toolkit at their disposal when it comes to data preparation, analysis, and presentation. The data is then used to draw inferences or suggestions.
The demand for data scientists is overgrowing. Companies increasingly collect large amounts of data and need someone to help them make sense of it all because the more data they have, the better insights and statistics they can produce. So, it’s not uncommon to find large and small enterprises capitalising on the value of their data.
Or you can say that analysing and breaking down this data became the heart of any decision-making process; should the company release a new product or not? Is it worth adjusting this service or coming up with a new idea? Should the company enter a new market, or is its niche more than enough?
Data science can be used in various industries, including healthcare, finance, retail, and more. To succeed in data science, you must be good with numbers and familiar with computer programming.
In the modern digital world, expansion or reduction can not be made without giving space to terms like data science.
To extract this data and turn it into valuable and meaningful insights, data scientists should proceed with some steps and procedures, starting with extracting, manipulating, visualising, and, finally, conducting maintenance.
We can summarise this process in four critical steps:
- Collecting data: This step involves collecting data from various sources. Data can be collected manually or through advanced tools.
- Preparing data: Once the data is collected, it needs to be filtered and prepared for analysis. This step involves removing any invalid or missing data and ensuring that the data is in a format that can be easily analysed.
- Analysing data: In this step, the data is analysed to generate insights. Statistical analysis, machine learning, and text mining are all viable options for doing this.
- Communicating results: Ultimately, you’ll want to share your findings with decision-makers and other relevant parties. Reports, slideshows, and data visualisations are valuable tools for this purpose.
That’s why professional scientists should have knowledge in many areas, including machine learning mathematics, programming, and different concepts of AI. They should also be proficient in understanding and identifying patterns and trends in the data.
After that, businesses use the outcome to make strategic, data-driven decisions. Data scientists can also assess the company’s performance and suggest necessary changes based on what they have figured out during their job to turn bland data into reliable information.
What are Common Applications of Data Science?
Many businesses use the concept of data science to build several great applications. It’s a game-changer for all industries. They don’t need to create a strategy and then figure out it is irrelevant.
Here is the list of data science applications:
• Predictive modelling can be used to make predictions about future events based on past data. It is often used to forecast demand, sales, or other outcomes.
• Segmentation is the process of dividing groups based on shared characteristics. Segmentation can be used for marketing purposes, such as targeting specific consumer groups with personalised messages or offers.
• Customer churn analysis identifies customers likely to stop doing business with a company. Churn analysis can help enterprises take steps to prevent customers from leaving, such as by offering them discounts or improving customer service.
• A better understanding of customers’ preferences can be gained by analysing customer experience, survives, and website traffic. A company like Airbnb uses data science to predict customer behaviour. That enables the company to address any issues and develop its service accordingly.
• Designing a more engaging education curriculum becomes possible by analysing data on student performance. To assist students in succeeding, teachers might zero in on specific areas where they are having difficulty and focus their lessons on those areas. Additionally, data science is sufficient for developing new assessment tools and tracking student progress over time.
What Are the Differences Between Artificial Intelligence and Data Science?
Similarities can be easily spotted regarding artificial intelligence (AI) and data science. Both involve working with data and using that data to create models or make predictions. So, in a sense, these two domains are connected. However, there are some critical differences between the two fields.
Definition
Data science is all about understanding data. AI is about making machines more intelligent to reduce human intervention.
Scope
Data science involves cleaning and organising data, performing statistical analysis, and creating visualisations to help make the data easier to understand. The superior knowledge, in this case, is the ability to analyse data based on past and present data. AI is concerned with creating algorithms that can learn and improve over time. So, AI is more about machine learning.
Important: Data science operates many tasks where data is the superstar. Otherwise, AI mainly needs data to create ML algorithms.
How it works
Data science involves four main steps: the collection of data, cleaning and organising the data, building models to find patterns, and interpreting the results—which means it entails preprocessing analysis. On the other hand, AI algorithms automatically identify patterns in data or make predictions based on past data. AI entails implementing a predictive model to anticipate future events. AI systems can also be designed to improve themselves over time through a process known as “machine learning.”
Purpose
Data science aims to make sense of large amounts of data. It uses techniques from statistics and mathematics to find hidden patterns. AI aims to create machines that can think and learn for themselves. That involves developing algorithms that can recognise patterns and make decisions on their own.
Career Level
Data scientists mainly take a more exploratory approach to effectively communicate their findings to others to help businesses make the most appropriate decisions. AI engineers are responsible for developing and working with algorithms that enable machines to learn and handle tasks that would otherwise require human intelligence. The leading role calls for making these machines take on projects autonomously, removing humans from the entire process.
Important: AI contains a high level of processing compared to data science, which might complicate the job of AI engineers as they have a lot to master.
Relevant areas
Data science embraces statistical and design techniques and development methods. AI focuses more on algorithm design, efficiency, conversions, development, and, as a final step, the development of these designs and outcomes.
Tools
Data science mainly depends on Python, Keras, SPSS, SAS, and R. Artificial intelligence tools include Scikit-Learn, Shogun, PyTorch, TensorFlow, and Kaffee.
Type of data
Data science uses many different data types, such as unstructured, semi-structured, and structured. Artificial intelligence depends on standardised data, mainly vectors and embeddings.
Models
Data science models are constructed to generate insights for decision-makers. AI‘s models are concerned with understanding the human mind’s processing of information to minimise human assistance.
Outcome
Data science conveys complex models to visuals, numbers, and insights using statistical techniques. AI sets up models that emulate human cognition for self-sufficiency patterns, meaning the machine can react confidently and appropriately without any human input.
Applications
Data science is used in internet search engines like Bing, Yahoo, and Google. Also, we can take advantage of this concept in any marketing or advertising field, and, of course, in finances and many more. AI appears in healthcare, transportation, manufacturing, automation, and robots.
Degree for professionals
Data scientists must have programming knowledge, but an academic certificate is not a must. AI engineers have to hold a very high degree in any related subject, such as machine learning, scientific processing, or software development.
Skills to nail
Skills in data science include having a solid foundation in tools like Python, Stata, or R. AI engineers will need to reach an expert level in algorithm design.
How Can Artificial Intelligence and Data Science Add Value to Your Business?
Indeed, we understand well how data is a critical ingredient for any business plan. It’s all about data. We also found how large businesses apply artificial intelligence and data science to leverage their operations to reach their goals and objectives.
But how? Every day, people generate tremendous amounts of data that will slow down any business, to be honest. But, more importantly, how can companies keep it up? It might represent a new challenge for small and medium companies, specifically.
It is always about using the right tools. Data science is the core of making information actionable, while AI helps business owners make the most of their assets.
Let’s see how AI and data science can contribute to your business.
Get to Know What’s Behind the Scenes
Artificial intelligence and data science will help you go beyond numbers by inferring meaning from data clusters in a readable way.
Make Accurate Predictions
Entrepreneurs can reach their own targets only if they acknowledge the possibility of succeeding or failing at any subsequent step. Then, they’ll be able to put their ideas into action.
Give the Business Real-Time Feedback
Time is money. One of the best benefits of AI and data science is that they give businesses access to the right time frame by analysing data through time series.
Maximise Innovation
They can do this by setting the right prices, finding the best customers, and allocating a reasonable budget depending on the company’s resources. These actions help businesses expand inventory and work in progress.
Gain and Retain Customer Loyalty
For example, a company wants to launch a new product with one of two options: a high-end or a low-end product. Data will help make a final decision. Customers usually turn from average buyers to loyal clients when they find what they ask for. That leads to a climb in revenues.
Improve Decision-Making
This is achieved by providing more precise and timely information. For instance, data science can analyse customer behaviour patterns and predict future trends.
Challenge Your Staff to Focus on Only What Matters
AI and data science will help your team manage their priorities to guarantee the best practices because they already know what is more important for your customers. After that, they will shift their considerations accordingly. Also, your employees will be aware of your business objectives and your products’ capabilities, which will give them insights into what to do and where they should be.
Define Your Target Audience
The prediction advantage of using artificial intelligence and data science includes identifying your audience more specifically, like their geographic locations, buying habits, personal traits, and emotional triggers, to attract them with a well-tuned message. That will greatly help your marketing efforts and initiatives with higher accuracy and eventually be useful in introducing investment perspectives.
Direct Action to the Right Trends
Artificial intelligence and data science outcomes will help businesses figure out new trends and reassign new tasks based on what data scientists explore. This discovery is healthy for business performance. Not just that, but you will help your enterprise stand out and increase profitability.
Make Use of Cutting-Edge Software
While both domains seem like rocket science, we can make the most of them in different and practical ways, such as with highly advanced platforms. In addition, many software programmes are based on AI algorithms that help business owners operate, manage, and mentor workflow. That will lead to an increase in team productivity.
Cut Costs
Many businesses can automate tasks and cut unnecessary labour expenses by adopting digital transportation. In addition, AI and data science can help companies find new ways to optimise their operations and save money. For example, data science can identify inefficiencies in a company’s supply chain or develop more effective marketing strategies. Also, enterprises will make groundbreaking decisions that will eliminate the possibility of spending a fortune on something that is already a lost cause!
Process Natural Language (NLP)
We can stay here for a while to explore why this feature will change the way of business management for good. NLP specialises in using AI to understand, read, and write the human language. NLP technologies will give many companies a boost to understanding every gesture, intended or unintended, for business development. This can be done through sentiment detection, topic modelling, and entity recognition.
Recruit and Retain Competent Employees
Hiring the best candidates is a challenge for almost all businesses. Resumes and interviews can only do the best job of figuring out who will add value to your company, and keeping the most productive employees can be an extra struggle. AI technologies can make a difference by digitalising the recruiting process. That will help the HR team find better potential employees faster. Moreover, AI and data science can activate their algorithms to read and filter resumes and give you insights about who will be worth pursuing.
Important: You can even take advantage of categorising features and let the machine identify how the best candidate can look based on specific personality traits. So, the company will have a comprehensive picture of its future employees.
Challenges of Artificial Intelligence and Data Science
In recent years, businesses have increasingly turned to artificial intelligence and data science to gain a competitive edge. But even though these cutting-edge technologies can be very helpful, they also come with unique challenges.
Here are some challenges companies can face when optimising AI and data science technologies.
• Keeping up with the increasingly changing landscape of AI and data science. Breakthroughs take place all the time, and businesses need to be prepared to invest in the latest tools and techniques if they want to stay ahead of the curve.
• Dealing with the massive amounts of data that need to be processed for AI and data science to be effective. Depending on the circumstances, this may be time-consuming and challenging and require expert-level knowledge.
• Understanding the complicated problem. One of the most common challenges when optimising AI and data science techniques is that machines could struggle with multiple predictive models. Then you will need more than one regression or classification model. Instead, this project will require an ensemble of models to receive the podium outcome.
• The need for analytical skills. As artificial intelligence and data science are relatively new topics, it could be challenging to be a superstar engineer who can handle projects smoothly, especially in terms of analytical skills. These skills are the milestone of any satisfactory outcome. The professional should break the problems into smaller and smaller ones to address the patterns more easily.
• Supercomputing power needs. To manage an artificial intelligence and data science project, your team will need a supercomputer. Machine learning and deep learning are the cornerstones of AI. These technologies need an ever-growing number of GPUs and cores to work together to build algorithms, and all of these need highly advanced computers to deal with these complex algorithms, which are not cheap at all.
• Over-reliance on AI and data science. Removing the human element entirely from some operations can hurt accuracy. You must have a good understanding of what is best for your business and who will execute a project successfully and create the utmost value for your customers. AI technologies can predict with 70%, 80%, or even 90% accuracy. But if your worker can do better, assign it to your employees.
• The need for more than one specialist. Depending on the shortage of a competent workforce, companies might need more than one professional to handle artificial intelligence and data science tasks. You may even need managerial-level employees, who could be a plus cost for small to medium enterprises.
• The enigmatic nature of deep learning. Technology is still unaware of the way models predict the output. In simple words, how a specific set of inputs can come up with a unique solution for different kinds of problems.
• Defining the need for a business problem to solve using AI or machine learning solutions. Most of the time, the answer is mainly based on implementing a tangled set of rules. So, engineers should be aware of the type of tasks that AI can handle. Also, define the kind of data to run using the right too. Finally, determine performance metrics that can be used to assess models.
• Lack of data. No data, no project. If the size of the data is limited, you will have nothing to train the models properly. You would need only internal data to complete your task. But when you have to get data from outside sources, your main job is to collect and bring good, important data. So, before even starting an AI or data science project, determine the data sources. Also, one of the common challenges here is that data formats can vary considerably. Since this is the case, data preparation is another responsibility of the technical team that will probably come up later, and it will take extra time.
• Data quality. The quality of the outcome you would gain depends on the data the AI systems are trained on. Good artificial intelligence services mean good data. Without it, machine learning engineers will face tremendous challenges and implementation difficulties because of biases. A good result comes from unbiased data. That’s when real change gets noticed. The right answer to this is to invest in developing a framework to achieve transparency to define biases in algorithms.
• Shortage of competent professionals. As the field is relatively new in the market, many professionals don’t have enough knowledge about AI’s potential. For example, many companies are still far from implementing AI development services for selling and managing products online. Not only do they miss an ample opportunity to grow, but they also don’t get trained on such innovative tools. As a result, the number of specialists is still limited.
• The absence of value metrics. AI and data science engineers often overlook these metrics when it’s actually the core of the whole process. It may result in the project failing upside down.
• Cybersecurity and privacy threats. As we said, better performance means using high-quality data to train the algorithms. On the other hand, the issue of storage and security is a serious concern. Companies need to access more data, which increases the possibility of data breaches. Not to mention that we’re talking about a global scale here. It’s not uncommon to find data about patients leaking on the dark web! That’s why ensuring the best data management technique for sensitive data is critical. Some companies have already started implementing a new way of dealing with data storage by not sending data back to the server after training their system. Only trained models are sent to the organisation.
Need more?
Consider this example: if you ask one of your employees to look at a picture and tell you if it’s a dog or a cat, they will definitely answer in less than a second, with an accuracy above 100%. But let’s see what it takes to do this task with AI.
First, you should have a deep learning model to conduct a simple study. It then requires unprecedented fine-tuning, a large dataset, perfect computer power, and hyperparameter optimisation. No, this is not the whole thing! It also needs well-defined algorithm modelling and proper, uninterrupted training. Then, a testing procedure is a must. Still, the outcome will not be as accurate as when a human responds, “It’s a cat!”
See?
It sounds like a lot compared to that easy task. But, unfortunately, it’s a million times more complicated than it requires.
Alternatively, businesses can avoid this hassle by using a service provider that uses pre-trained models. It entails training specific deep learning models using millions of images to maximise accuracy. But again, the problem is that the machine would continue to show errors and struggle to reach human-level performance.
Let’s assume you want to know more about these domains, then where should you start?
Which Should You Start Learning First: Artificial Intelligence or Data Science?
It’s a kind of tricky question. In general, it is all about your background, experience, and the technical career path you want to take. Artificial intelligence and data science are essential, in-demand fields with a lot of overlap. So how do you choose? Here are some factors to consider:
• Your goals: What do you want to achieve? Learning AI is a must if you want to create AI applications. But if your goal is simply to analyse data, then data science may be the way to go.
• Your background: Do you have experience in programming? If so, learning AI will be more accessible for you. If not, then data science may be a better choice.
• Your interests: What sort of things do you enjoy doing? If you like working with numbers and analysing data, then data science is probably a good fit.
If you are interested in AI, you should start by learning a programming language like Python. Then you can move on to learning AI algorithms and theories. However, suppose you’re more interested in data science. If so, you should begin by studying an appropriate computer language for statistical analysis, such as R. After that, you can learn about specific data science concepts and methods.
However, you can start building knowledge about these domains:
- The basics of programming: You need to be able to code in at least one programming language. If you’re interested in artificial intelligence and data science, Python is a wonderful language to learn, but there are others you may also consider.
- Linear algebra and calculus: These mathematical disciplines will help you understand the algorithms used in AI and data science.
- Probability and statistics: You need to be able to analyse data, which requires a strong understanding of probability and statistics.
Other Terms Related to Artificial Intelligence and Data Science
Many terminologies have emerged as artificial intelligence and data science continue to grow throughout the world, and these concepts undoubtedly have a great impact on our daily lives. Whether you have a business or want to pursue a career in one of them, let’s see some of the terms you should know.
Machine Learning
Machine learning (ML) is a field of computer science that uses statistical techniques to allow computer systems to learn from and train on data without being explicitly programmed. The term “machine learning” was coined in 1959 by Arthur Samuel.
Computational Statistics, another field concerned with generating computer predictions, is closely connected to ML and sometimes overlaps with it. However, the topic benefits significantly from the contributions of mathematical optimisation in theory, practice, and methodology.
Deep Learning
Deep learning (DL) is a subset of ML based on artificial neural networks. It uses large datasets and powerful computers to learn complex patterns in data. Deep learning can be used for various applications, including image recognition, natural language processing, and recommender systems.
Natural Language Processing
Natural Language Processing (NLP) is a branch of artificial intelligence that deals with the interaction between humans and computers using natural language.
Using natural language processing (NLP), programmers can create software to comprehend and interact with people realistically by reading and responding to their words. In addition, NLP applications can process and understand large amounts of data to extract meaning from it.
NLP is an important tool for artificial intelligence research as it allows computers to understand human language better. Moreover, NLP has many applications in medicine, finance, and customer service.
Big Data
Big data describes large volumes of structured and unstructured data that organisations can now collect thanks to technological advances.
But it’s not just about the size of the data; it’s also about what organisations can do with that data. With the most innovative tools and technologies, businesses can use big data to glean insights that would otherwise be hidden in all that information.
That’s why big data is such a hot topic today. It helps businesses make better decisions, run more smoothly, and even develop new products and services.
Internet of Things
The internet of things (IoT) is an entire and tangled system of physical devices and other items embedded with sensors, electronics, software, and connectivity, enabling these objects to collect and exchange data.
IoT has been a core buzzword in the digital industry for years, but many people are still unsure what it entails. Simply put, IoT is the interconnection of physical objects and devices (think anything from cars to home appliances) with each other and the internet. This device interaction can then collect and exchange data about said devices’ usage and performance.
Business Intelligence
Business intelligence (BI) refers to software and hardware solutions for collecting, organising, analysing, and presenting information to make strategic business decisions.
This term is sometimes used synonymously with business analytics; however, there is a distinct difference between the two. Business intelligence covers a broader scope of information management, including querying, reporting, online analytical processing (OLAP), statistical analysis, forecasting, and data mining. On the other hand, business analytics focuses on applying statistical analysis and modelling techniques to data to answer specific business questions such as “What are our sales for this quarter?” or “How many customers did we acquire last month?”
Data Analysis
Data analysis entails processing data via various procedures, including inspection, cleaning, and modelling, to find insights, provide recommendations, and aid decision-making.
Data analysis has many facets and approaches. It can be used to examine past trends to predict future events, test hypotheses about cause-and-effect relationships, or develop models to describe complex systems. Data analysts may use statistical techniques, mathematical modelling, machine learning, or data mining to find hidden patterns and correlations in data.
Data Mining
Data mining (DM) is the field of extracting valuable information from large data clusters. DM is about sorting large amounts of data to find patterns and trends. It also can be used to find hidden information that can be used to make business decisions.
DM is a relatively new field that has emerged in the past few years. However, it is becoming increasingly popular as businesses realise the value of extracting hidden information from their data. Data mining can improve customer service, target marketing campaigns, and detect fraud.
Data Engineering
Data engineering is about managing data according to an organisation’s specific needs. This process includes designing, building, and maintaining data infrastructure, as well as developing tools and strategies to ensure that data is effectively used.
Data engineering is critical for any organisation that relies on data to make decisions. Without adequate data management, an organisation will quickly become bogged down in information and unable to use its resources best.
What is the Future of Artificial Intelligence and Data Science?
In the coming years, AI, data science, and other related technical fields are expected to advance significantly in various areas. The future will witness several of the most significant innovations seen in the last age. Thanks to the data explosion, the growth resulting from IoT, and massive exposure to social media, experts find that the next generation will revolutionise how machines can react. As the number of people who use social media keeps going up, companies can use this free data to improve their customers’ experiences.
What about artificial intelligence? Can AI be a proxy for the human brain?
It’s an emerging industry, for sure. One area where AI and data science are expected to have a considerable impact is healthcare. Some hospitals already use AI-enabled robots to assist with tasks such as dispensing medication and checking vital signs. In the future, AI could be used even more extensively in healthcare, for example, to help diagnose diseases and develop personalised treatment plans.
Another area where AI is expected to have a major impact is transportation. Testing of autonomous vehicles is already underway on highways across the globe, and their widespread use is anticipated within the next few years. AI will also likely play a role in developing other new transportation technologies, such as flying cars.
Also, manufacturing will change a lot in the future, supported by AI machines. Many companies have already developed integrating devices to do many tasks related to assembling and stacking. This technology will help businesses run complex operations smoothly.
In education, we will use many digital textbooks, and online certificated education will expand to be the first choice for many students. In addition, AI-powered tools will be utilised to identify how students receive information through facial analysis, which unveils something. That will help education institutions determine how satisfied students are and who’s struggling or bored in the class.
This data will be insightful to enhance every individual’s needs and craft curriculums that are more engaging and appealing for different levels.
Media will continue to benefit from innovative AI tools by gaining an immediate sense of complex features to offer more informative news and reports. And you could find some jobs disappearing, like those of editors or maybe writers (hopefully not).
Offering better customer service will become more accessible by expanding the capabilities of chatbots or even running calls with robots to cut off the hassle of piling up complaints and orders without responding. Also, AI performs various business development tasks without human intervention, like building brand awareness and customer interaction.
Many experts see that AI-backed tools can beat our human minds in every cognitive task, but it will take more time than high-tech companies guess.
Overall, it is clear that AI and data science will have a significant impact on many different aspects of society in the coming years.
Takeaway
• Artificial intelligence and data science are different terms with different applications and functions.
• There are many similarities and differences, but the key is always related to data and how you use it to create something more relevant and meaningful.
• Optimising your business’s AI and data science technologies is inescapable for any business’s success.
• The benefits of data science and artificial intelligence are countless. Just remember the costs you can save when you understand the market and your audience.
• Artificial intelligence and data science still face many challenges, but they can be handled using appropriate strategies and ethical practices.
• One of the biggest challenges many companies encounter is finding the most relevant data.
• There is a vast lexicon of AI and data science-related phrases. There is no need to master them all, but you should have a firm grasp on the ones that will serve you best as a company owner or a student.