AI vs Data Science

Artificial intelligence (AI) and data science are two core technologies alongside robotics, the internet of things (IoT), quantum computing, and others are the force behind the fourth industrial revolution. The fourth industrial revolution is defined by the generation of and demand for the massive volumes of data that are gathered and analyzed to inform decisions. AI and data science are two 4IR technologies that are transforming the world. They both leverage big data in different ways. Data science uses advanced analytics to discover hidden patterns and extract valuable insights from big data for strategic decision-making. AI and machine learning, on the other hand, require big data to train models that learn and evolve in making accurate inferences and predictions. Also, AI technology automates data collection, processing, analytics, visualization, and data modeling processes which would otherwise be too complex, time-consuming, and labor-intensive if done manually. 

According to an IDC report, global spending on AI technologies was projected at $97.9 billion by 2023. This is a reflection of the growing interest in AI. Equally, the demand for AI and data science skills has soared with artificial intelligence engineers and data scientists at the top of LinkedIn’s emerging Jobs Report with 74% annual growth in the past four years. The good news is that as enterprises embrace digital and revolutionary technologies like AI, online learning for these technologies is widely available and accessible. Whether you enroll in an online structured curriculum or opt for self or project-based learning, there is no limit to what you can do with the skills you will acquire.

What is Artificial Intelligence?

Artificial intelligence is the science of building smart machines. It involves programming computers to mimic human intelligence in performing certain tasks including learning, reasoning, creativity, and planning from input data to facilitate decision-making and problem-solving. AI has been adopted widely across most industries and sectors for applications such as risk mitigation, fraud prevention, credit rating, image recognition, speech recognition, language translation, autonomous vehicles, personalized shopping, and recommendation systems among many others. 

AI is divided into two categories based on applications:

Applied AI: Applied AI advances software applications to enhance their efficiency in performing a task with human intelligence while adapting and increasing accuracy over time. Applied AI is applicable to specific application areas such as computer vision, speech recognition, robotics, natural language processing, autonomous vehicles, sentiment analysis, and others. 

General AI: General purpose AI is an unconfined type of AI that programs machines to learn, understand, and execute all kinds of tasks or solve any problem intelligently just like humans. Unlike applied AI, general AI is focused on managing all tasks like their human counterparts. 

The role of AI engineers is to build end-to-end AI solutions to improve efficiency, productivity, problem-solving, and decision-making for business operations, product development, and service delivery. They collaborate with data scientists, business analysts, and other business and IT professionals in deploying AI models that will help achieve business goals. 

What is Data Science?

Data science is an interdisciplinary field that uses scientific techniques to discover hidden patterns and trends, and extract useful information and insight from vast volumes of structured and unstructured data for strategic planning and decision-making in business. Businesses, both large and small across different industries and domains, are leveraging the value of data science insights for growth and profitability.  

The Data Science process involves data collection, cleaning, exploration, analysis, visualization, modeling, and interpretation. Data science can make use of AI/machine learning as a data analysis technique. This field has been instrumental in business for assessing and mitigating risks, market trends, and understanding customer behavior.  

The role of a data scientist is to use statistical methods, and other tools to make the most of the data available to a business. As we have seen, data science is a multidisciplinary field that rounds up statistical analysis, mathematics, design, communications, and programming to drive data-driven decisions.

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Data Science vs. Artificial Intelligence: A Detailed Explanation

Even though there is a big overlap between AI and data science skills, the two fields complement each other, and collaborate in the same environment, they are different in many different ways. At the core, AI powers some data science operations while data science is not entirely dependent on AI. Data science is a much broader discipline and AI is just one area of data science. 

Let us understand the differences between artificial intelligence and data science. 

• Goal 

• The goal of AI is to train models to mimic human intelligence in learning, adapting, and becoming autonomous to carry out such tasks as predictions accurately without human intervention. AI improves efficiency and productivity while minimizing errors in workflows. 
• The goal of data science is to gather data, process it, and analyze it to discover hidden patterns and trends as well as gain insights from large volumes of data, a task that would otherwise be too complex to be carried out manually.  

• Process 

• AI process involves the implementation of various models for different purposes. For instance, implementing a predictive model to analyze patterns in historical data and predict future outcomes. 
• Data science processes involve the collection, cleaning, processing, analysis, interpretation, visualization, and modeling of data to discover hidden patterns and trends and extract valuable insights for business strategic planning and decision making.  

• Scope 

• AI is much narrower in scope and could be categorized as one of the analysis techniques in data science. 
• Data science is a broad field that encompasses all statistical analysis, design, and development techniques used for extracting information and insights from data.  

• Tools 

• AI uses computer algorithms and tools including TensorFlow, SciKit Learn, Keras, and PyTorch which facilitate the automation of various AI processes that involve the manipulation of massive data volumes. 
• Data science tools include programming languages like R and Python, techniques like AI and machine learning, as well as other statistical techniques that are used to perform data analysis. Data science uses many more tools in addition to AI because it is a broader field and also because it uses more steps to process data and extract insights. 

• Skills 

• AI requires knowledge of programming languages like R, Python, and Java, as well as statistics, exploratory data analysis skills, software design, and data modeling skills. 
• In addition to basic computer science knowledge, data science roles require professionals to have knowledge of programming languages like R and Python, statistical analysis, machine learning, as well as data processing, and wrangling. 

In summary

In recent times, giant enterprises like Amazon, Google, IBM, Alibaba, Facebook, and others have leveraged AI to develop intelligent systems that can run without human intervention. Today’s enterprises cannot do without data. Thus both AI and data science roles are critical to the success of any business. Depending on the size and needs of the business, you may come across enterprises with data scientists, and AI engineers as well as sub-roles like machine learning engineer, deep learning engineer, and deep learning scientist positions that take on more specialized roles. Yet still, these two fields complement each other. Datas sciences helps businesses to make data-driven decisions while AI helps businesses to run their workflows more efficiently and productively.