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Data Science vs Artificial Intelligence and Machine Learning

Data Science vs Artificial Intelligence and Machine Learning

01-Dec-2020

What is Data Science?

The field of Data science comprises of a very wide range of domains. It is mostly related to data systems and analytical processes which are run on those data systems. It involves maintaining data sets and extracting patterns out of them to draw meaningful insights and derive meaning. Data scientists utilize a big set of tools, technologies, applications, principles, and algorithms to derive meaning out of random data clusters. It is very difficult to monitor and store data in today’s world, where organizations are producing vast amounts of data almost daily. This amount is rising exponentially as more and more organizations branch out into different verticals and find new functions and domains for themselves. So to monitor and keep a track of this data, techniques of data modelling and data warehousing are required. Data scientists bring those to play to track the ever-growing data set and attempt to make some sense out of the seemingly random and jumbled mess of information. Data scientists use the information that they extract from these data sets to assist businesses and organizations to make business decisions, achieve their short-term and long-term goals and realize their product vision.

Applications of Data Science

The domains of information technology and computer science have advanced to such an extent that they have enabled data science to foray into new forms of computation. Now Data Science counts in its fields predictive analytics, Data mining, Data examination, Business intelligence, Deep learning, AI, and much more. Some people criticize Data science by claiming that it is merely a fad and that the present hype around it will fade away in time. But the truth is far from what is claimed. The truth can be said to be that the drive in the direction of Data science is growing bigger and bigger as businesses and organizations accumulate large volumes of data. The organizations desire to make use of that data to bring changes and improvements to their routines and procedures. As organizations develop their businesses more and more the volume of data that they have to retain becomes larger and larger and unwieldy. The organizations desire to run analysis and evaluation on the generated data and they desire to hire skilled data scientists specifically for this purpose. Thus Data scientists will face no problems in being assimilated into organizations around the world.

Also Read: Data Science Certification - What Are Its Benefits?

The different formats which data scientists apply to analyze historical data are -

Predictive causal analytics – Data scientists make business forecasts with the help of this model. The predictive model specializes in assigning specific measurable quotas to various business actions. It is a very effective model for any businesses trying to comprehend what will be the effect of any future business moves.
Prescriptive analysis – This model calculates which options have led to the most success in the past and helps businesses set their goals by prescribing the actions which have the most probability of resulting in a success. This is done by drawing inferences from the predictive model.

Data scientists take the help of a huge array of tools and technologies including Hadoop, R, Python, SQL. They also take the help of distributed architecture, data visualization, and statistical analysis to extract patterns and draw meaningful insights from large data sets.

What is Artificial Intelligence?

Artificial Intelligence is a very popular term in the technological world. It has been widely misunderstood because of popular movies and novels misappropriating the word and using it to portray technologically advanced robots who are conscious. The real meaning of the term Artificial Intelligence is the field in which computer scientists attempt to simulate human-like intelligence in machines. Artificial Intelligence aims to give machines the ability to perform reasoning and logical processes similar to human beings. It means giving machines the ability to understand complex relationships and make actionable decisions.

Artificial Intelligence enables machines to interpret the environment around them and take certain paths of actions based on what they find and observe. This is almost the same as replicating human intelligence. The main principle of Artificial Intelligence is to help machines learn from experience. This is done by feeding them the right kind of information and training them to interpret the data correctly. They also have to be able to set their mistakes right without any outside aid or interference. All this is done with the aid of a collection of mathematical algorithms. This is where the fields of Natural Language Processing and Deep learning come in to help Artificial Intelligence experts. With techniques from those two fields, they can make machines identify patterns and draw inferences.

Applications of Artificial Intelligence -

Automation – One of the main applications of Artificial Intelligence is automation. One can identify similar, repetitive, and relatively simple tasks and automate them with the help of Artificial Intelligence programs. They can set up reliable systems which perform the tasks which saves a lot of time, energy, and resources, especially if the tasks in question are high-volume.

Intelligent products – With the aid of Artificial Intelligence, even conventional products can be transformed into sleek and revolutionary commodities. For instance, in the case of conversational platforms, pairing artificial intelligence with customer chat software can produce extremely helpful chatbot assistants which smoothen the process of interacting with the customers or with other employees and greatly increase productivity.

Progressive learning applications – Artificial Intelligence can be used to train machines to be able to judge and decide on a particular course of action based on their environment and their past experiences. Then they can even function independently of their programmers in many cases and perform the desired functions. This is done with the help of advanced Artificial Intelligence algorithms such as predictors and classifiers.

Analyzing data – For training machines to evaluate choices and make decisions by following reasoning processes similar to human beings’, data is required. Thus it becomes necessary to first train machines to identify and pinpoint the right kind of data. This is done by feeding them datasets chosen according to neural networking algorithms which specialize in teaching machines the right habits, to choose a human term.

What is Machine Learning?

In the case of Artificial Intelligence the machines have to be equipped with mathematical algorithms in order to enable them to make decisions. This approach of writing programs to teach machines to follow certain paths of action is not the end all and be all of artificial intelligence. There is another approach which is such a sizeable part of artificial intelligence that it is its own field and is a widely known subset of Artificial intelligence. It is known as Machine Learning.

The approach in machine learning uses huge datasets of past experience based data to teach machines how to evaluate the environment they are present in and make the right decisions. Thus it aims to equip machines with independent learning techniques so that they don’t have to rely on programmers to program the exact specific algorithms into them for making decisions. Initially the machines have to be programmed with algorithms, but under machine learning the algorithms only involve teaching the machines to observe and study data and experiences to extract patterns. The machines can subsequently use these patterns to build their own reasoning system to follow logical lines of reasoning and make decisions.

Going into some details, the specific way Machine learning is implemented is explained as follows. A machine is fed many examples of the correct way of solving a problem. With such a large sample set of solutions, the machine quickly learns how the solutions were arrived at and learns to solve the problems itself. Once this model of training the machine produces a mature machine which produces high accuracy results reliably it can be deployed to a production environment where it can be utilized in solving problem sets such as predictions or classifications.

Relationship between Data Science, Artificial Intelligence, and Machine Learning -

Artificial Intelligence, as has been previously mentioned, is the science of simulating and replicating human intelligence. Data Science is the science of utilizing that same human-like intelligence to extract patterns from data, draw accurate inferences and make the right decisions. Machine Learning is a subset of Artificial Intelligence which uses past data to learn from experiences. Therefore Data Science serves as a valuable and essential link between Artificial Intelligence and Machine Learning.

Data scientists have to take the help of Artificial Intelligence techniques, methods, and algorithms to identify and extract patterns from data. Artificial Intelligence functions on the principles of Perception, Planning, Action, Feedback of Perception. The perception stage refers to the act of observation. When a machine observes its surroundings and environments, or observes data and forms data sets and records, the act is taken to be in the stage of perception. The stage of planning refers to the analysis and evaluation of the data that a machine has gathered. The machine runs several artificial intelligence processes and algorithms upon the data and analyzes and evaluates it. Once it has outputted the desired results from the data it utilizes those results in making decisions, which brings us to the next stage. Action refers to the stage where the machine draws inferences and makes decisions based on the information which it has extracted from the data sets.

For eg. A self driving car from Google may detect too much traffic on the road ahead which it was planning to take. Then it will analyze Google’s massive data sets for alternate routes to its intended destination. After proper analysis and evaluation of the data sets, and the past history of the road ahead with regards to the traffic on it, the car will then decide whether it should take one of the available alternate routes or wait on the same road and let the traffic disperse. This process of making a decision is included in the Planning stage.

Data science uses different parts of these principles to find solutions to various specific problems. Taking the first stage of Planning as a point, data scientists try to identify and extract patterns from data sets with the help of data. In the next stage of the principles’ loop, the stage of Planning is present. Data scientists operate on that principle in the following way. They consider two main aspects of the Planning stage. They are to find all possible solutions or to find the best solution out of all possible solutions.

Machine learning can be understood properly by seeing it as a link between Artificial Intelligence and Data Science. We say this because machine learning is the process of drawing learning experiences from data sets over time in the form of experiences. Thus Data science is an integral part of machine learning and so is artificial intelligence. Machine learning is basically what helps in achieving the goals set by the data scientists after they have derived results and solutions for specific problems with the help of Artificial Intelligence.

FAQs about Artificial Intelligence, Machine Learning, and Data Science -

Is Machine Learning the Same thing as Data Science?

Machine Learning and Data Science are not even remotely the same. They are two distinct domains of technology, although they do intersect in some ways. Machine learning is centered around enabling machines to learn from their experiences and execute many tasks, Data science is centered around analyzing and evaluating data and helping businesses draw meaningful insights and make decisions on the basis of that data. It helps businesses to identify and understand trends.

Is Machine Learning better or is Data Science better?

It should be mentioned at the outset that we can not compare the two fields to decide which field is superior as they have completely different applications and domains of knowledge. It is a little like drawing a comparison between Science and Arts to see which is superior. That said, one has to accede that Data science is much more popular by far today. Most of the companies around the world rely on data to make more informed and knowledgeable decisions now. Machine learning on the other hand is still an emerging field of computer science and has not seen such a wide adoption as the field of Data science. It is definitely a highly relevant domain of knowledge for many industries and will certainly be in greater demand in the future.

Is Data Science required for Machine learning?

Data science is not strictly required to function in the field of machine learning but having a basic knowledge of Data science will definitely prove to be helpful. Having said that, the knowledge and concepts of Data analysis will be much more useful to operate in the field of machine learning than knowledge of data science. Having an intermediate level of knowledge in programming languages like R, Python, and Java is required to handle large data sets, clean them, and to use them to program machine learning algorithms. Many machine learning courses contain tutorials and basic classes on the subjects of Data analysis and programming languages.

What is the future of Data Science?

Data science has a bright and booming future coming up head. Most businesses and industries rely heavily on the knowledge and insights that Data science provides them with. To proceed without relying on Data science means to be left behind, to put it simply.

You can also read: Future Scope of Data Scientists

Can a Data Scientist become a machine learning engineer?

Data science and machine learning have a lot in common. The technologies, algorithms, and even programming languages used are very similar between the two fields. So a data scientist will not have any major difficulties in transitioning to a career as a machine learning engineer since they will have already worked with the libraries, tools, and applications of machine learning when they worked as a data scientist.