Artificial Intelligence has been around since the mid of 20th Century. Until the late ’90s, there was a lack of desired processing and computational power to implement AI, and it seems impossible to acquire efficiency in this field as a futurist of that time knew would be possible in the near or far future. The rebirth of Artificial Intelligence in a previous and ongoing decade, because of the discoveries made in the processing domain, the advent of powerful Micro-Processors, and advanced GPU’s, coined terms like Machine Learning, Data Science, and Data Analytics.

Nowadays, whether you are reading a newspaper or surfing on the internet, it’s common to come across terms like machine learning, data analytics & data science, etc. But what do these magical spells do? And why should one ponder over these terms? People are a bit confused in answering the “what” and “which” questions in a debate about Data Science vs Machine Learning or Data Science vs Data Analytics. In this article, we will try to solve the riddle by differentiating these areas one by one and try to dive into the vast ocean of AI and Data, as much as possible.

Data Science

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Data science as revealed by its name is all about data. As Science is a general term that includes several other subfields and areas, data science is a general term for a variety of algorithms and methodologies to extract information. Under the curtain of data science, the roots exist in the form of scientific methods, detailed mathematics, statistical models, and a variety of tools. All these tools and methodologies are used to analyze and manipulate data. If any method or technique can be used as a tool to analyze data or retrieve useful information from it, it likely falls under the umbrella of data science.

Have you imagined using data science to make smart business decisions? Working with data science and its tools involves the process of connecting the dots, extracting shreds of evidence and useful patterns from the data, and finding effective connections that can be utilized to efficiently acquire the business goal.

The most precious thing of our times is Data, as every single tech company is involved in the competition of collecting overwhelming amounts of data, which is also referred to as Big Data. The more data they obtain, the more business insights they can obtain. Data science enables us to find hidden patterns in data. 

For instance, we can discover that a backpacker who went to Bangkok for a vacation is most likely to visit Phuket or Pattaya during the same tour. If you’re running a tour company offering leisure trips to beautiful beaches of Thailand, you might be eager to know this tourist’s contact info and other insightful details for the targeted marketing or other purposes, and it will be easier for you to offer to customize tour package to the said person based on his interests, schedule, and destination.Data science is widely used in such scenarios. Organizations are using data science tools to develop recommendation engines, and predicting customer’s interest, and other similar stuff. The more data they can get, the more accurate their predictions will be. This goal is achieved by using a variety of intelligent algorithms that could be applied to available data to get the desired results.

You might be thinking that this whole scenario seems a lot like Machine Learning and your mind is still confused in a clash of Data Science vs Machine Learning. Don’t worry, your confusion is natural. Because the algorithms that are being used to extract useful insights from datasets are machine learning algorithms. Machine learning is the secret ingredient in the recipe of data science under which we can make accurate predictions, precisely target the actual goal, and discover insightful patterns in the data. Let’s discover Machine Learning in detail.

Machine Learning

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Machine Learning is a sub-methodology of a very general and vast field of AI. We also regard Machine Learning as one of the several ways of implementing AI. As revealed by its name “Machine Learning”, it is used in scenarios where we want to make a machine learn and extract from the overwhelming amounts of data.

Making machine learning is also called training a machine, and there are several methods to do so, such as supervised learning, unsupervised learning, partially-supervised learning, and reinforced learning. In some of these methodologies, a user directs the machine on the elements of learning, like making it differentiate between the independent variables (input), and the dependent variable (output). In this way, the machine learns the proportionality between the dependent and independent variables hidden inside the data that is fed to the machine or a system. This data which is provided to train the machine is called the training set. Once the learning phase finishes, the same machine learning model is then tested on a test set, which the model has not sighted or experienced ever. Once the model or algorithm becomes efficient enough to produce accurate outcomes, the model will be deployed on the system for real-time use, and it starts making predictions based on learned data in the real-world scenario.

There are different methodologies in Machine Learning that can be used for predicting outcomes, problem-solving, etc.

Some commonly used algorithms and statistical methodologies in Machine Learning include:

  •         Linear Regression
  •         Polynomial Regression
  •         Support Vector Regression
  •         Decision Tree Regression
  •         Random Forest Regression
  •         K-Nearest Neighbors

These are all statistical methods. With the help of advanced Machine Learning libraries such as SciKit Learn, one can use all these regression techniques without prior knowledge of advanced mathematics or statistics.

We have discussed Machine Learning and Data Science and can differentiate between both but wait a minute. What on earth is called Data Analytics? Let’s discover in detail which side is heavier and carries more impact in Data Science vs Data Analytics, and what Data Analytics is.

Data Analytics


If data science is the hanger that serves as a general hub to contain tools and techniques, data analytics is a specific chamber in that hanger. It is linked with data science, but more concentrated than its parent field as instead of just uncovering connections between different elements of data, data analyst deals with the sorting of data, combining separate data segments to establish fruitful outcomes that can assist an organization in achieving their objective. It is done by grouping data into two segments.

  •         A data that organizations know they know
  •         A data that organizations know they don’t know

The discoveries made by this technique are later used to measure past scenarios, solving present problems, or making future predictions on the basis of what is available at the time of analysis. In short, Data analytics transports data from deep insights into the effective influence by uncovering the hidden patterns and valuable trends in data elements and making them synchronized with the Organization’s actual objective. Thus, acting as a tool that is more goals oriented than Data Science in General.

Why The Differences Matter

These negligible differences while discussing Data Science vs Data Analytics or Data Science vs Machine Learning, can cast different shadows on the goal’s aspect. As the job roles of Data Analyst, Data Scientist, and Machine Learning Engineer are considerable.  Experts in these fields have different prerequisite knowledge and background. A debate rises during the recruitment process while announcing a vacancy to hire these experts then it becomes obligatory for the companies to use suitable terms to attract the right people for the right job. Data analytics and data science can be used to extract and uncover different insights from data, Machine Learning involves the development, training, and testing of Machine Learning Model to develop the intelligent machine. No doubt all these three areas possess immense importance in the IT world but they won’t be used alternatively for each other. Machine Learning is usually involved in making Models for pattern recognitions, biometrics recognition, and developing intelligent machines, In contrary Data analytics is used in areas like healthcare, tourism, and stock markets, while Data Science deals with the study of patterns in internet searches and the use of resulting insights for digital marketing purposes.

Data science serves as a vast platform and provides ground for the development of intelligent systems and deploying machine learning technology. It also serves as a home for subdivisions like Data Analytics.

Nowadays, every organization is dependent upon the systems that enable them to utilize computational strategies to dive deep inside the vast ocean of Data. Machine learning has an undeniable impact and is playing a crucial role in setting up the roadmap about how modern businesses should be run now and in the future. Because of the vastness of impact caste by these fields, the companies as well as employees must understand the difference between Machine Learning, Data Science, and Data Analytics.

Despite the differences, Machine Learning, Data Science, and Data Analytics are vital assets for the future of IT and they have one thing in common: Data, which the experts of the aforementioned fields have to deal with. The organizations that want to lead the technological revolution in today’s era, should deploy these methodologies for the successful understanding of Data to make their organizations not only survive but thrive.


One Coin Two Sides, One is Dark and One is Bright

There are a lot of aspects we can discuss these different technologies and terminologies. The area of AI and Data is so deep that millions of people across the globe have restricted their profession and life in better understanding and evolution of these technologies for the bright and comforting future of mankind. There also exists a group of futurists and experts who believe that AI and Humankind can’t go side by side and results would be devastating as this advancement towards intelligent machines will push humanity towards extinction.

Dark Side

Yuval Noah Harari, an Israeli public intellectual, historian, a professor in the Department of History at the Hebrew University of Jerusalem and the author of the bestselling book 21 Lessons for the 21st Century, argues that Artificial intelligence has made humankind vulnerable in the same way as climate change and nuclear war and a technology race in genetics could threaten the entire humanity.

“People should realize humankind is now facing three existential threats that cannot be solved on the national level,” he said. “They can only be solved on the global level.”

“These threats are nuclear war, climate change, and technological disruption, especially the rise of Artificial Intelligence and bioengineering. Artificial Intelligence and biotechnology could destroy what it means to be human.”

He said that an “arms race” in the Artificially Intelligent World would lead to the destruction of humanity.

“Whoever wins this race it doesn’t matter, humanity will be the loser.”

Bright Side

But another side of the story is, none of us know a bit about the future, and time travel is still not yet possible. At present we’re here in this world, experiencing the revolutionary advancements of AI, and one should welcome this technological revolution. We should only focus on how we can use these technological advancements for the betterment of mankind and make this world a safer and better place. There are so many organizations and private firms researching how we can use Data Science and Machine Learning in the health sector to detect diseases and predict upcoming viruses for early preparations to save as many lives as we can. For instance, there’s a lot of research going around how Machine Learning Models can help detect cancerous tumors, the early problems of data unavailability due to the privacy of patients are somehow solved after the remarkable development of deep fakes technology.

The finance sector is also using AI to filter out fraudulent financial transactions from the real one. As mathematical libraries like Keras and TensorFlow evolve at a fast pace, the more unfolds are being made in the capabilities of detecting fraud. The banking and finance sector is now able to extract insightful patterns from the transaction habits of a customer, user’s account history, and credit marking of millions of people’s data to detect and immune loan and insurance frauds which was once an almost impossible task like finding a nail in the ocean.