Data science and AI have become two most talked about fields. A career as a data analyst, AI engineer or data scientist can fetch you a high salary as well as a fulfilling job experience. Data Science involves knowledge of a lot of subfields (which we have discussed later). AI is yet to get there but is picking up fast with the advancement in technologies and successes in fields like neural networks, fuzzy logic, NLP, machine learning, etc…
We have reached the level where we can ask Alexa or Siri to set up appointments, switch on the TV, play our favorite songs or movies, google and present information, play games and much more. There are self-driving cars that try to learn and improve based on past experiences, Robots that can walk and talk like humans and perform tasks as instructed, chatbots that make analytics easier with a better interface and messaging tools, etc…
You would think…
Are these examples related to data science or AI?
All of these examples require data collection and processing. In machines like Alexa or self-driving cars, for the system to gain some intelligence (AI), it has to be trained.
Data is the key.
But the data that is received from various sources is not as perfect as you would think so…!
It is raw, unstructured and can have a lot of mistakes. The data has to be corrected to prepare for analysis.
The analysis part is where all the learning happens – i.e. the system gains artificial intelligence.
Huge data sets, also called Big data are fed into the system and algorithms are applied for training. The algorithm is then adjusted and improvised based on accuracy and once near accurate results are achieved, the testing data set is fed into the system to check the accuracy and build the final model.
But the mystery remains….
Why should we compare Data Science and AI?
Hearing about the word Data science leads to many questions in our mind – what is data science? How is AI related to data science? Is AI part of data science? What does data science include? What does AI include?
We will attempt to answer all these questions and more in this article by comparing data science and AI, and also understanding the similarities.
What is data science?
The simplest answer would be – it’s a field where data from various sources is cleaned, transformed and analyzed to make certain predictions that can help companies take better business decisions for the future. For example, based on a Facebook user’s browsing history i.e. pages visited, post engagements and other browsing data, Facebook can determine his preferences. Facebook can then show the user some targeted content which the user would most probably like. Data Science is a vast subject which includes both technical and non-technical aspects. Many professionals – big data analysts, domain experts, business analysts, data engineers, statistics analysts, programmers etc… work together to complete the data science lifecycle. A typical cycle looks like –
- A problem statement is given by the client with the required parameters and criteria
- Data is collected from various sources like data centers, cloud, surveys, etc… and cleaned.
- Data is validated, prepared and processed to transform it so that it can be analyzed
- Various algorithms are used to analyze and train the system so that they can find patterns and trends in data
- Based on the findings, reports can be generated with possible solutions to the problem and further business decisions can be made
The entire process involves thorough domain knowledge, research, and analysis, data engineering capabilities, computer science and programming knowledge, statistical and mathematical aptitude, understanding of machine learning tools and business mindset.
What is AI?
Humans are known to possess intelligence i.e. they can think on their own and make their own decisions based on past experiences, similar behaviors, and pattern matching. What if a machine can do the same? This intelligence possessed by machines to make decisions based on certain algorithms is called Artificial Intelligence or AI. The goal of AI is to create machines that can think on their own and make accurate decisions. There are many branches of AI that can be used to solve complex real-life problems –
- Machine learning
- Deep learning
- Expert systems
- Natural Language Processing
- Fuzzy logic
Machine learning is currently used in many industries to perform predictive data analytics. Machine learning can be done with or without human supervision. There are many applications of machine learning, for example, spam email detection, google search engine optimization (SEO), Facebook photo tagging, weather forecasting, recommendation systems, live map traffic updates, etc…
There is a lot of progress in the fields of deep learning, NLP, Robotics as well, but those are more complex and need more research – we are not quite there yet.
How are both related?
Data science and AI both are vast and growing. While data science is quite an established field and the topmost field in the technology world now, AI is still in initial stages and a lot of research is underway to reach a level where machines can think and act like humans.
Data science is a complete process in which machine learning (ML) plays a pivotal role in training a system and allowing it to find trends, patterns, behaviors, and predictions that can help businesses make important decisions for the future.
ML is a branch of AI and involves familiarization with a lot of algorithms as part of the learning process. Machine learning includes supervised learning, unsupervised learning and reinforced learning methods for building algorithms that process huge datasets and interpret useful insights.
Why do people compare both?
In a lot of places, people tend to use both Data Science and AI interchangeably. Since they are related and data science depends on machine learning algorithms, most people think that AI falls under the data science umbrella. But that is not true. Here is a simple depiction of data science and AI and how they are related.
We see that as of now ML is the subfield that joins data science and AI. In future, data science would be required to cater to each of the branches of AI – we would need to collect and process data for deep learning, NLP, robotics etc… That means for advancement in AI, we need data science and for performing data science steps we need AI.
Which is bigger – Data science or AI?
While data science uses part of AI known as machine learning for insights and predictions, both fields are vast in their sense. AI is still far-fetched in terms of 2nd and 3rd level where machines can think like humans and surpass them respectively. However, data science is currently used in every possible domain for various purposes. Learn about some of the most important data science applications here.
AI vs Data Science: Head to Head Comparison
We understood the basic difference – how part of AI – ML – is used in data science. However, other differences will help you understand more about data science and AI. Here is a head to head comparison between both –
|Data Science||Artificial Intelligence|
|includes retrieving useful insights from huge datasets||imitate human abilities like speech, ability to perform simple tasks, comprehend and respond to natural human language, etc…|
|it is a whole process, which involves extensive operations on data||more research and learning based process mainly focused on machine learning and deep learning algorithms|
|Raw data obtained can be structured or unstructured||Data can be in raw or unstructured form, or as vectors, embeddings, etc…|
|AI is just a part of the entire data science lifecycle, where machine learning algorithms are applied||AI requires data which can be prepared and transformed through data science tools|
|Tools like R/Python, Tableau, SAS, Excel, TensorFlow, Matlab and many more are used||Tools like Keras, Scikit Learn, PyTorch, H20, Microsoft Cognitive Toolkit, Theano, Caffe, etc… are used|
|For data science, ML algorithms are sufficient for analysis and problem-solving.||AI needs ML, deep learning algorithms, fuzzy logic, NLP and more to enhance the intelligence levels of machines|
|Data science is the most promising career now, and best paying job in the industry||AI is still in its early stages, however, the industry is now recognizing and hiring more AI engineers. AI has a bright future and is believed to be the highest paying job.|
|Data science aims to solve business problems, where most of the processes are automated, however, a lot of human intervention is still required.||AI aims to create a mechanism for computers to think and make wise decisions, eventually without human intervention|
|Data science works on facts, data, experience, and knowledge||AI also involves perception, cognitive thinking, neural networks along with factual data and algorithms|
Through this article, we covered some important differences between AI and data science and also found the link between both – data and machine learning. Both AI and data science work hand in hand and one depends on another for the success of the overall technology. While data science fully focuses on data, AI also involves intuition, thinking, and perception – qualities of a human brain.
Understanding the difference also allows you to decide which career path is most suited for you – being a data scientist or an AI engineer. Data science has evolved nicely and will continue to rein in the next few years. AI is fast-growing and a lot of focus is now on building machines that can imitate humans, which means in the coming years AI will be equally important and a lot of jobs will come up for research and development of AI systems.
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