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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.
Data Science
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.
Machine Learning
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 Learningâ€, it is used in scenarios where we want to make a machine learn and extract from the overwhelming amounts of data.
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.
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.
Conclusion
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.
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.