Data Science, Machine Learning and Artificial Intelligence: Understanding the differences

in StemSocial2 months ago

The inter connectivity between Data Science, Machine Learning and Artificial Intelligence has made it difficult to clearly differentiate between Data Science, Machine Learning and Artificial Intelligence, although, this misconception exist majorly outside the academics settings. While some people believe that Data Science is an essential ingredient of Artificial intelligence, some people's opinion is exactly the opposite of the former.

Data Science, Machine Learning and Artificial Intelligence are among the three prominent fields in the field of computing today, their emergence arose virtually at the same time. Additionally, most of the applications moving the world today comprise of these three dominant fields, a typical example of such applications includes Robot (sophia), Google’s self-driving cars and Tesla self driving car.

However, the reason for this divergent opinions is not far fetched, It is due to the inter relationship that co exist within the three fields. The three fields are so inter oven to the extent that people, especially those who are not a professional and do not have an in depth understanding of the three fields will not fully understand their differences. Some are even of opinion that Machine learning is the same as artificial Intelligence and some could not differentiate between Data science and Machine Learning and some are of opinion that the three are the same, however, the three are though similar but they are separate fields, they are belongs to the same field of computing but with each of them has a specific application usage .

Image source: Flickr

Data Science

Data Science is a field of study that analyse, visualize and explore data in order to discover hidden information which could not be visible by ordinary naked eyes, the source of this data may be from company's database, organization's database, government's database, education's database, student's database, social media's database. e.t.c.

Hive as a social media platform maintains a set of records of all the users including their details such as country of destination, year of join, number of post, followers, following and their subscribed communities. For instance, consider the data published @blocktrades three (3) days ago, it shows us a graph of US traffic, along with the list of some countries with their population. The data was studied, analysed using certain tools by the cloudflare before sending it out to his email. Such an extraction of raw data from database(s), Processing the extracted data for analysis, exploring the processed data, performing an in-depth analysis on such data and finally communicate the results of the analysis to the intended domain is what Data Science encompasses.

You can check for your country and the number of users here.

Data Scientist achieves this through the extraction of relevant and related data from a pool of database which can be a stand alone database (as a single database) or having more than one database configured in different locations. It is the duty of Data Scientist to decide and apply the right tools, algorithms, application and principles to reveal this hidden knowledge.

Artificial Intelligence


Image source: Flickr

Artificial Intelligence

Just like its name indicates, it is the human created intelligence, you can consider an Artificial Intelligence to be a machine that can replicate certain level of human intelligence, that is, the intrinsic ability of human intelligence to learn and able to draw conclusion based on antecedent, new conditions and trying conditions.

For such a machine (Artificial Intelligence) to be able to carry out such a task of manipulating the environment like human being, it definitely must be able to apply certain level of knowledge received in the past just like human beings do, but the fact that scientists have not found a way to create 'human brain like' which can be incorporated into machines then information (data) must be stored inside such a machine (Artificial Intelligence), this information (data) must be trained and tested many times in order to be sure it will be able to draw conclusion when a newly related data is tested on it.

Thus, how do we process the stored information (data) in the machine so as to be able to manipulate the environment just like human beings.


Image source: Flickr

Machine Learning

The information (data) stored in the machine (artificial intelligence) needs to be processed, trained and tested many time in order to be able to manipulate its environment. This is exactly where Machine Learning comes into a play.

Machine learning(ML) is basically the study of algorithms that can be applied on a processed data in order to discover patterns from such data after being trained and tested many times, this pattern will then be used to take a decision on future occurrence of related data.

It should be noted that machine learning does not follow traditional programming. It caries out the test and training on data using an algorithm. Basically, Machine Learning algorithm is divided into: Supervised Learning, Unsupervised Learning, Semi supervised learning and Reinforcement Learning among other types which don't belong to any of the classification above and some that make use of two or more of this classification.

Alan Turing test
In a real life scenario, to be sure that something is a replicant of the other, it must be tested many times before such a declation will be made, likewise in computing, especially creating a robust system like artificial intelligence which will replace human being, any slight error may be brutal as such Artificial Intelligence machines also need a standard test that will be able to confirm the replicability of Artificial Intelligence machines.

The late and great Alan Turing test has been known and used as a standard test to measure whether an artificial intelligence machine has the potentials to exhibit behavior close to or approximately equals to human beings, and this is popularly known as the Turing Test.

Final Remarks!

  • Both Data Science and Artificial Intelligence make use of Machine Learning because Machine Learning is basically deals with the computer algorithms which can learn and discover pattern from the pool of data in the database without following the old or traditional way of coding, it should be able to mimic human intelligence, some of the commonly used machine learning algorithms is Artificial Neural network, Linear Regression, Decision Tree, Logistic Regression, K-Means, Random Forest. e.t.c

  • While Machine Learning is specifically categorised as a subfield of Artificial Intelligence unlike Data Science which can make use of other statistical tools without totally relying on any of machine learning algorithms.

  • Data Science works on raw data through sourcing, cleaning, processing data, exploring data and presenting it for analytical purposes, while Artificial Intelligence (AI) works on large amounts of data and apply machine learning (intelligent algorithms) so as to make computers learn and act automatically.

  • Examples of applications of Artificial Intelligence (AI) includes Chatbots, Robots, and Voice assistant while example of Machine Learning includes Face recognition system, finger print recognition system, Spotify. Examples of Data Science includes Healthcare analysis and Fraud Detection.

Thanks for reading through, your reaction and feedback will be appreciated.


1. Artificial Intelligence, Machine learning, deep learning and data science - What’s the difference?
2. What is AI? Ingredients for Intelligence
3. Data Science vs AI: Difference Between Data Science and Artificial Intelligence
4. Data Science vs Machine Learning and Artificial Intelligence
5. Machine Learning: What it is and why it matters


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