Data Science vs Artificial Intelligence vs Machine Learning is an article that needs to be written.
With so many data-related terms being thrown around, you need to understand just what fits where to get a grasp on the Data Science world.
Clearing the misconceptions or confusion is the most important thing. It is a common phenomenon to confuse these three spheres- Data Science vs Artificial Intelligence vs Machine Learning.
It is quite understandable if this is you- “Data Science or Machine Learning, Data Science vs Artificial Intelligence?”
You are reading this post and it means you wish to stay clear of the confusion and build your basics on these concepts.
Data Science vs Artificial Intelligence vs Machine Learning
Let’s take a closer look…
When you think of searching for something on the Internet, what is the first thing that comes to your mind?
Yes, Google is the first go-to search platform!
Apart from giving you search results, the search engine giant also tells you how many searches resultsresult in your query yielded and the time it took to show you the results. This is possible with the help of an organized way of using data, i.e., Data Science.
Internet Search is one of the basic examples of the use of Data Science. Few others include predicting customer behaviour, fraud detection etc.
The daily task of to and fro commuting between workplace and home is an exhausting exercise, thanks to the blazing traffic.
How do you deal with this regular problem? That’s right! Checking the traffic beforehand helps you decide which route to take. Thanks to the GPS Navigation Services, you know if you have to take an alternative route to be in time for the crucial board meeting.
The data collected from the analysis of daily traffic for various places helps the machine to do congestion analysis and predict the regions with high congestion, daily.
Who doesn’t know about the helpful assistant from Google, the Google Assistant?
This virtual assistant is one of the perfect examples of Artificial Intelligence. It not only collects the data based on the voice input but also recognizes the user based on the previous activities on Google account and elsewhere.
Google Assistant engages in a two-way conversation and you aren’t lonely anymore!
Try saying- “I am bored.”
The faithful Google Assistant would promptly revert with- “Bored? What is this? Here are some activities to entertain you.”
Be prepared to answer a few interesting questions or read some amazing facts.
An interdisciplinary field that uses data to generate insights and put it to use for the benefit of the business.
It uses scientific methods, processes and systems to derive useful information from both structured and unstructured data.
Data analytics, statistics and other related methods are used in this field to solve real problems of the company.
A part of artificial intelligence, systems that actually provide a framework to build sophisticated AI components.
The field of study that deals with the research and development of computer systems, working on tasks requiring human intelligence.
The AI-powered machines can mirror cognitive functions, such as learning, speech recognition, decision making and problem-solving etc.
Data Science vs Artificial Intelligence vs Machine Learning: Feature-Based Comparison
|Feature||Data Science||Machine Learning||Artificial Intelligence|
|Focus Area||Processing large datasets to generate insights and solve the problems of the businesses and take better decisions buildbasedbuild based on build build||Build framework to make machines learn and take decisions on the basis of the instructions given to buildingbehaviour||Building machines that can imitate human behavior and take decisions and perform tasks that would otherwise require human intervention|
|Characteristic||Apply the right techniques to analyze the data using statistics, visualization and machine learning are also utilized in this field.||A subset of artificial intelligence. A process through which a machine transforms into an artificial intelligence creation.||Parent area that houses data science and machine learning. Aims at making systems that work like humans are realized here.|
|Fundamental Problem Statement||– Classifying data
– Processing data
– Deriving insights
– Taking decisions
|– Develop a powerful and dependable framework that learns how to recognize patterns and the instructions given to it and works accordingly.||– Recognize systems to be made intelligent
– How can this be possible?
– Ethics of AI
|Significance||– Customer segregation
– User-oriented product development
– Make relevant and useful predictions
|– Fraud detection
– Filtering email
– Detecting information or security breaches
|– AI is everywhere in all walks of life; almost every future innovation will be AI-powered careerfraudulent|
|Career Opportunities||– Data Scientist
– Data Engineer
– Data Analyst
|– ML Engineer
– Chief Data Scientist
|– AI researcher|
Applications of Data Science, Machine Learning and Artificial Intelligence
With continuous development in the three powerful realms- Data Science, Machine Learning and Artificial Intelligence, there are lots of applications that have come up for human use and ease their lives.
Machine Learning, an application of AI and Data Science, is the intersecting field of the two.
Therefore, the applications developed in these specific fields, together shape our future mode of living.
1. Product Recommendation
You search for a product on any e-commerce site and then go back to your Facebook account or browse through other sites and there you see the same products.
This is because Google tracks your browsing history and makes recommendations even when you are on other sites.
These product recommendation ads are one of the most useful applications of machine learning.
2. Social Media Features
Similar Pins feature on Pinterest, People You May Know feature on Facebook or the automatic tagging feature, are the popular applications of Machine Learning.
Social media platforms display Target Ads and improvise your news feed to give you a better and useful experience.
In these features, the machine learns from the user history and works on improving the user experience.
3. Fraud Detection
By comparing the types of transactions and other details of the applicant like its history, profile etc., the machine can differentiate a genuine transaction from a fraud one.
Not just this, banks and digital payment apps are also making use of ML to prevent money laundering and keep an eye on the types of transactions going on between the buyers and the sellers.
4. Innovations in Healthcare
With the advent of AI in the healthcare domain, collecting patients’ data and analyzing it with increased precision is now possible.
The healthcare sector now witnesses improved innovations that aim to offer affordable health services, accessible to all.
Used more as an augmentation tool, AI is widely being involved in healthcare as a supporting hand for the doctors and nurses; it can now do tasks that are not possible for the human eye.
These are some of the applications of Data Science, Machine Learning and Artificial Intelligence.
Data Science vs Artificial Intelligence vs Machine Learning: What does the future hold?
Machines are now getting ‘intelligent’ with the continuous technological advancements in AI; it’s there in almost every field.
The process that starts with data collection, moves a step further to make machines learn and finally ends with making machines intelligent to help humans.
You must have heard about the humanoid robot ‘Sophia’. It is one of the perfect examples of what AI is capable of.
Several organizations and governments have increased their investment in visionary projects and research in this field as they are aware of the benefits and the potential of Data Science, Machine Learning and Artificial Intelligence.
An artificial intelligence-powered business will not only be the most profitable one but would also have an edge over others.
|What does it mean?
– Data Science generates insights.
Data Science, Machine Learning and Artificial Intelligence are the fields that work in conjugation with each other.
These might not make sense as a standalone dimension.
Because these are interrelated to each other. So the Data Science vs Artificial Intelligence vs Machine Learning debate is a pointless one.
Picture the fields of Data Science, Machine Learning and Artificial Intelligence to be three employees of an organization- one each from Human Resource, Accounts and Sales departments.
These employees who have a differen job jobsbut are essentially working for the same organization.
Did you get it?