Data science is the key driver behind streamlining and innovation in processes of any organisation. This mixture of techniques, algorithms and analysis methods has revolutionised industries and careers. Artificial intelligence or AI has made data analysis possible at unprecedented speed. Understanding the users, their preferences, behaviours and future actions are also easier and more conclusive with AI. The debate of data Science vs artificial intelligence is evolving with technological advancements, however, their base remains the same. Understanding these terms is necessary for professionals looking to upgrade their stature in the organisations, or for a career transition. This debate of Data Science vs Artificial Intelligence will be clearer as you read ahead.

Data Science vs Artificial Intelligence – Understanding the Difference

Let’s have a look at what these terms mean individually and understand their purpose to start comparing. While we can say that the ultimate aim of these two is the same in the present scenario, they differ in processes, applications and in how they collect and present data.   

What is Artificial Intelligence?

Artificial Intelligence is a set of algorithms that perform autonomous actions. They interact with users, collect the data and improve on their functioning by understanding user behaviour. These mechanisms are able to perform complex functions within seconds. To enable accurate decision making through AI, the initial data supplied to it should be accurate and detailed, this is where data science plays a role. Moreover, we are currently using ‘Artificial Narrow Intelligence’, meaning the machine does not have complete autonomy on actions but can perform the functions it is programmed to perform. 

What is Data Science? 

Data science is a process that uses computer science, statistics, management and visualisation processes to predict user behaviour patterns. It can help in increasing the overall operational efficiency of a company. In order to do this, a data scientist uses several resources as input, filters the usable data, compiles relevant data and through various tools, creates reports and statistics on the basis of which decisions are taken. 

With big data coming into the picture, data scientists use AI to collect the data and derive deep learning through it.  With AI, it is possible to go through uncountable databases with ease and filter out the unnecessary information. It aids in using data science skills in the best way. 

You can get more insights into data science and the best industries to enter if you want to be a data scientist with this readAlthough, Data Science And AI differ in many ways, which can be understood by taking the debate of Data Science vs Artificial Intelligence forward.

Data Science vs Artificial Intelligence – How do they differ?

  1. Data collection
    Data science begins with the aim of solving a problem. It collects data through advanced mathematics and statistics that show how the problem arises and devises the right solutions for them. AI contrarily uses computer algorithms to predict future events. In the process, AI continuously collects user data to analyse and understand the trends. This user data, combined with machine learning, and other tools form a major part of how data science processes collect data. Hence, AI also becomes a tool through which data is collected for data science analysis. 
  2. Learning techniques 
    Machine learning is a part of AI. Through statistics and other tools, ML enables AI to learn data and initiates predictive behaviours. Deep learning is a subset of machine learning that mimics the neural operations in the human brain. Through this, AI is able to evolve and give better results with time. On the other hand, data science is based on analytics which uses data. Through these analytics, data is segregated and presented in a way that is easy to understand, which enables the user to make decisions. Collection of data for these predictions depends on AI and other tools like linear algebra, probability, etc. which are called advanced mathematics. Hence, AI learns through machine learning and DS uses a combination of these to perform data analytics.
  3. Decision making:
    AI algorithms were created to behave like humans i.e. learn and evolve like them. As AI is in its initial stages, it learns and predicts behaviours through machine learning and deep learning. It takes predictive decisions through an intelligence-based process. Similar to the functions of a human brain, it is able to modify its own behaviour when it predicts a change in user behaviour. For example, chatbots on various websites respond to the questions based on the input they receive from the user. When the question changes, the answer changes accordingly.  Data science is a skill-based process of taking decisions. Data scientists analyse patterns in data, group them and present them. Where AI can make its own decisions and initiate an action, data science cannot take the action. The scientist and people involved in decision making understand the data and define the next steps.  
  4. Applications areas
    Data science enables better decision making by pinpointing what is missing from a process and what can be changed. With the help of artificial intelligence and machine learning, data science is being used in various industries like cybersecurity, healthcare, advertising etc. AI on the other side, is being used to read our queries sent on the search engines, to suggest what we should enquire about. It is predicting mass behaviour that is used in marketing, e-commerce, IT, robotics, and several other industries. There is no limit to how much growth can be seen in the application of AI and data science, especially with the progress in technology.

If you are interested in having a career as a data scientist and want to dive deep into the applications of data science, you can refer to this article. Read about which areas are impacted by data science and how. 

Data science and artificial intelligence are used interchangeably because in the layman’s eyes they are performing the same functions. Although the techniques and applications vary, these technologies are interdependent. The data collected while using data science analysis can be used to make AI function better, as having solid data is one of the prerequisites of AI at the initial stage. Also, data science analysis has benefited immensely with AI enabling the computation of extensive data. With further developments in technology, both will improve and enable more complex analysis. 

Hope this blog posed cleared your doubts about data science vs artificial intelligence. If you are interested in learning and making a career in data science, do check-out Springboard’s online certification courses on data science and artificial intelligence that come with a 1:1 mentoring-led, project-driven approach and offers a job guarantee. These will give your career the right start. Join thousands of other students and embark on your journey of detailed and affordable learning.