Difference between Machine Learning, Data Science, AI, Deep Learning, and Statistics

You must have come across the terms like data science, machine learning, artificial intelligence, deep learning, statistics, applied mathematics, Internet of Things, operations research, etc. They are all interrelated with each other and play a great role in the modern world of digitization. These might sound like technical jargons if you don’t understand the importance in a correct way. Therefore, in order to understand these terms in a better way, it is important to know, what are they?

Data Science, Machine Learning, AI, Deep Learning, and Statistics

Software Company India

Let’s go through what are the meanings of the terms in a brief way:

  • Data Science: It is data directed science that is all about multi-disciplinary fields of scientific methods, systems, and processes in order to gain knowledge from data in various pattern, which can either be structured or dismantled, similar to that of data mining.
  • Artificial Intelligence: It is an intelligence that is not exhibited by the humans but the machines. It is all about the problem solving and learning phase of the machines, which improves in due course of learning and is also known as cognitive function. This has been discovered in order to implement intelligence in the machines and to create expert systems that will help in the path of technological evolution. It is packed with reasoning, problem-solving, and learning of the computer systems. 
  • Machine Learning: It is a part of the Artificial Intelligence or AI, which enables the computer systems to automatically learn and enhance with the help of various experiences, without the need to be explicitly programmed. Machine Learning emphasizes the development of the computer programs in order to access data and utilize it to educate them.
  • Deep Learning: It is a part of the Machine Learning and deals with the algorithms which are influenced by the function and structure of the brain, also known as artificial neural networks. It is all about educating the machines with the help of the learning data representations in contrast to the task-specific algorithms. The learning can be monitored, partially monitored, reinforced, or completely unmonitored depending upon the need for the system.
  • Statistics: It is a part of mathematics that works with the analysis, collection, presentation, interpretation, and organization of data. It plays a huge role analyzing the data that is to be used for data mining. 

Data Scientists & Their Types

The people who are responsible to fulfill the criteria of the data science are termed as data scientists. They have a huge demand pertaining to the trend of data science. The people involved in this career are differentiated as per their capability and field of study. The types of data scientists are as follows:

  1. Mathematician
  2. Statistician
  3. Machine learning scientists
  4. Data engineer
  5. Actuarial scientist
  6. Software programming analysts
  7. Special data scientist
  8. Digital analytic consultant
  9. Business analytic practitioner
  10. Quality analyst

According to the recent analysis, data scientists have been categorized into Type A and Type B. A refers to Analytics whereas B refers to Building. Type A scientists are capable to analyze data whether or not an expert in it. However, they need to be specialized in forecasting, modeling, statistical interface, etc. Type B is similar to the type A scientists in the statistical background but they are the expert coders and also highly skilled software engineers.

The data scientists have a prominent presence in any phase of the project of data science like data exploratory stage, data gathering stage, statistical modeling, and also in the phase where the existing data is maintained.

Machine Learning and Deep Learning

Both the terms might sound similar but have huge differences in the functionality. Before indulging in the differentiation of data science and the machine learning, it is important to get your hands on the deep learning. Deep learning is all about the algorithm of image and voice recognition. It predicts the video you would like to watch or showcase the product you would prefer to buy. It is a revolutionary change in the digital industry which has successfully established its existence. On the other hand, machine learning refers to a group of algorithms that prepare a data set to predict or take proper action in order to customize a few of the systems. This is primarily used to segregate the good database from the bad, based on the history of the data and purpose.

There are some contradictions regarding the machine learning and deep learning. Some consider deep learning to be an intense arena of the machine learning. Here are the reasons for the claim:

  • Artificial Intelligence or AI: This branch of technology was invented to make the life of the humans easy. This was done by implementing the human intelligence into a machine, eradicating the physical capabilities. It included planning, recognizing people and voices across the world, translating, speaking, performing business transactions and creative activities, etc. 
  • Natural Language Processing or NLP: It deals with the written language and is a part of the artificial intelligence.
  • Machine Learning: It is all about segregating the mechanical aspects of the data introduced in a system. This process needs to be monitored by an external resource to prove the efficiency. 
  • Deep Learning: This is a type of machine learning and works as per the composition blocks of the same category to obtain a favorable outcome.

Machine Learning and Data Science

Data science is a huge area and also covers the machine learning and statistics. Data science covers all the aspects of end-to-end data analytics that includes analysis, validation, preprocessing, deployment, and interpretation whereas on the other hand, Machine Learning emphasizes on the techniques utilized in the validation, analysis, and interpretation phases. In contrast to this, data science encompasses a broader area of technology that might or might not be derived from the machine or mechanical process. The data can also be collected from the clinical trials or can also be manually accumulated depending on the demand of the system. In a broader sense, the data science involves the following areas:

  • Distributed architecture
  • Data integration
  • Automated machine learning
  • Dashboards & BI
  • Data visualization
  • Data engineering
  • Automated & data-driven decision
  • Deployment in production mode

All the branches are not utilized as a part of the operation in some of the organizations but the scientists implement the principles to sort the issues regarding the same.

Machine learning helps analyze a huge amount of data with precision within a short frame of time in any Software Company India or other firms. This opens the prospect of identifying the profitable options as well as the dangerous loopholes.