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Data Science vs Machine Learning and Artificial Intelligence
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Bharani Kumar Depuru is a well known IT personality from Hyderabad. He is the Founder and Director of Innodatatics Pvt Ltd and 360DigiTMG. Bharani Kumar is an IIT and ISB alumni with more than 17 years of experience, he held prominent positions in the IT elites like HSBC, ITC Infotech, Infosys, and Deloitte. He is a prevalent IT consultant specializing in Industrial Revolution 4.0 implementation, Data Analytics practice setup, Artificial Intelligence, Big Data Analytics, Industrial IoT, Business Intelligence and Business Management. Bharani Kumar is also the chief trainer at 360DigiTMG with more than Ten years of experience and has been making the IT transition journey easy for his students. 360DigiTMG is at the forefront of delivering quality education, thereby bridging the gap between academia and industry.
Table of Content
Words like Data Mining, Data Science, Machine Learning, Artificial Intelligence, and Big Data have become commonplace among techies as a result of the development of technology throughout time. After all, in our digital age, they are the keywords. Whether you are a tech enthusiast or not, it is critical to comprehend the fundamental meaning of these phrases in order to thrive in the competitive climate of today. Here's where we step in to explain what AI, Data Science, and Machine Learning genuinely imply and how they vary from one another.
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Data Science
Being one most widely practised data-driven techniques, Data Science can be explained as the extraction of relevant information or insights from data sets all around the world. All of which is done in order to help an organization in taking better decisions and to reach its desired goals.
Data Science uses the process of data modelling and data warehousing for uninterrupted data sourcing. Thereby making it a very crucial element for any business to succeed. Why do you ask? Well, because Data is Revenue. The more you have, the higher are your chance to scale your business. With more business insights being driven out, you can easily gain knowledge about user behaviours towards your product or find your next client. This is why, companies all over the world, are using data science to predict user behaviour, create recommendation engines, and much more. Some general use of data science includes Automatic decision-making systems, Predicted analytics, Recommendation Systems, Tactical optimization, and Social research.
Role of a Data Scientist
While data science provides a vast array of advantages. The data scientist's role is to collect information from many sources and draw important conclusions from the information so collected. Tools including hypothesis testing, statistical modelling, machine learning algorithms, and visualisation techniques are used to do this. In addition, a Data Scientist must comprehend the data from a business perspective and make precise forecasts in order to assist in the company's business choices.
Therefore, if you intend to pursue this job, you'll need a unique mix of abilities in order for employers to consider you for a position.
- Having strong knowledge in SQL Database coding
- Highly experienced in Hadoop, Python, R, SAS, Scala
- Strong analytical function skills
- Being able to work with unstructured data and craving out the data from it
- Finally, the knowledge of Machine Learning
- This is because Machine Learning is used to perform predictive reporting and also to discover patterns from the data.
Machine Learning
Artificial intelligence is thought to be implemented through machine learning. Both do, however, have some key distinctions, which will be covered in more detail. Machine learning, often known as ML, is the process of employing computers to extract knowledge from massive amounts of data using algorithms, then applying that knowledge to anticipate future analyses of the data. As a consequence, it tweaks and alters the system's original algorithm to deliver even better outcomes.
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Components of Machine learning
There are three basic models of Machine Learning. These are Supervised, Unsupervised, and Reinforcement Learning.
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Supervised Learning:
In this kind of model, the algorithm gains knowledge from previously labelled training data before assisting in the prediction of the results of upcoming forecasts. This paradigm is excellent for using in real-world calculation issues.
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Unsupervised Learning:
Unlike supervised, in unsupervised learning, there is no labelled data hence it allows the model to work on its own, allowing the user to perform more complex processing tasks. Also, algorithms with this model have access to both retrieval-based and generative learning approach.
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Reinforcement Learning:
The reward feedback system is used in the case of learning through reinforcement. It lacks any sort of response key that may serve as a marked or unmarked function. This means that the entire model is implemented through a process of trial and error, which yields a delayed but long-term payoff.
In addition to this, ML uses various algorithms, like decision tree regression, K-nearest neighbours, Polynomial regression, Simple Linear Regression, and so on.
Only after the machine has been trained and tested enough for reliability and accuracy, the ML model can go into production.
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Artificial Intelligence
The reward feedback system is used in the case of learning through reinforcement. It lacks any sort of response key that may serve as a marked or unmarked function. This means that the entire model is implemented through a process of trial and error, which yields a delayed but long-term payoff.
Learning, logical thinking, and self-correction are among the main capabilities of AI computers. This enables the AI to develop further as it gains access to additional data. Each fresh batch of training data causes it to evolve.
Classification of AI
Artificial Intelligence is generally classified into two different sections.
- General Artificial Intelligence: This refers to making the machines apply skills in activities that require thinking and reasoning. This, in turn, provides more autonomous learning and problem-solving opportunities for AI.
- Narrow Artificial Intelligence: Unlike general Artificial Intelligence, Narrow AI shifts are used for very specific tasks. Also, in terms of reality, it is the Narrow AI that is within the reach of researchers and developers.
Although AI-powered machines often undertake the task by copying human intelligence. But here are times, when it goes way par of the normal humanly capabilities.
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The Relation Between Data Science, Machine Learning and Artificial Intelligence
It is clear from the foregoing that there are connections between all three sectors. However, it appears that both artificial intelligence and machine learning are covered by data science. AI and Data Science are joined by machine learning. Data Science does, after all, depend on ML algorithms to provide the projections for its views. And using the precise findings generated by ML, AI technologies may provide successful solutions.
A significant amount of data is also necessary for ML algorithms to function correctly and effectively. Data Science provides for this excess of data.
Thus, it may be said that ML serves as the link between AI and data science, the other two sciences.
Data Science vs Machine Learning and Artificial Intelligence
As of now, we are aware of how each of the aforementioned fields functions separately. It is clear that everyone of them is distinctive in some manner.
| Factors | Data Science | Artificial Intelligence | Machine Learning |
|---|---|---|---|
| Definition | It is defined as a concept to deal with big data which includes processes like data extraction, data cleaning, data preparation, and data analysis. | It can be defined as a collection of situations and techniques which are related to making the AI powdered machines more intelligent to solve those situations. | It is defined as the process of using algorithms to use data and learn from it in order to give a very high accurate result. |
| Scope | It has a lot of data operations | It includes the use of Machine Learning | The machines improve with experience. |
| Type of Data Uses | Structured and Unstructured | Embeddings, Logic, and Decision Trees | Statistical Models |
| Tools | Apache Spark4, MATLAB, SAS2, MySQL, RapidMiner | Azure Machine Learning, Caffe, CNTK, Keras, Pybin | BigML, TensorFlow, PyTorch, KNIME |
| Applications | Optimizing Real-time shipping routes, Detecting and predicting diseases, Automating digital ad placement, Personalized shopping recommendations. | Voice assistants like Google Home, Alexa, Chatbots, Intelligent Cyber Security, AI-Powered Home essentials | Facial Recognition, Traffic Prediction, Online Spam or Fraud detection, Speech Recognition. |
| Average Salary (in INR) for fresher | 6 Lakhs Per Annum | 6 Lakhs Per Annum | 5 Lakhs Per Annum |
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Some other key differences to be pointed out.
- Both Data Science and Machine Learning consist of various statistical methods, whereas in the case of AI, it only uses computer algorithms
- Data Science is usually based on strict analytical evidence available, whereas AI is based on using the use of human intellect on a machine while ML is just a subset of AI
- While both Data Science and AI are broad terms for multiple disciplines. It is ML which fits subtly in both
In final analysis, it doesn't matter which is superior; what matters is how one may achieve success in any discipline while still relying on information from other professions. A data scientist may easily transition into a job in machine learning or artificial intelligence, though. Nothing can be identical to the other two. We can come to a conclusion by acknowledging that none of these advancements are possible without the availability of data—and data alone.
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