Data Science vs Machine Learning
Data science is a field that uses multiple disciplines comprised of various processes, scientific methods, and algorithms to draw out knowledge from data. Machine learning is the technical study of algorithms and statistical models done in a scientific manner which the computer system uses. The computer uses these models to do a particular task relying upon artificial intelligence.
Why Data Science?
Data science is important because it helps the business organizations to extract the necessary actionable insights from the Gigabytes of data. With the help of this data, organizations can make proper strategies related to the business. Data science helps the organization to incorporate the statistics and get deep learning. This helps in the betterment of decision making and hiring. The company can get a clear knowledge about the new talents from several social media platforms, job searching websites and corporate databases. The companies also get the ultimate benefit of convenient risk management from data science.
The companies can find the right place to sell the exact product while exploiting data science. An organization can easily make the new product according to the customer behavior of a particular place. With the help of data science, it becomes easier for the sales and marketing department of an organization to get a clear note of the customer behavior at the minute level. Apart from providing reliable information about the risks, it also helps the organization to grab the opportunities. It also provides the data scientists, an opportunity to set a proper workflow in a particular organization.
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The scientists dealing with data science always make sure that the organizational staffs have a profound knowledge of the analytics data. Efficient data scientists are hired by organizations that can test the data. They check the basic matrices of the data enabling the organization to make positive changes.
Why Machine Learning?
Machine learning is important because it provides the computer with the learning ability without any explicit programming. The primary focus of machine learning is to provide specific algorithms to the computer as it gets trained to perform a particular task. Machine learning has got a close relationship with two fields like mathematical optimization and computational statistics. The methods present in the Machine Learning are unsupervised learning, supervised learning and reinforcement learning.
Machine Learning is important for many reasons. It enables the computer systems to do various tasks like text generation, playing games and image recognition. The works that Machine Learning can deliver are password changing or account balance checking. The machines can deliver excellent decision making when it is injected by a systematic algorithm of machine learning. Thus, the need for live agents can minimize in the professional field. Organizations dealing with varied businesses are more prone to use the Machine Learning algorithms as it can minimize the resources.
Accuracy is the keynote of machine learning. It is seen that the algorithms made nowadays are more accurate than the algorithms made in the initial days. The people related to Machine Learning are keen to look forward and make the artificial intelligence proficient by the algorithms that can make the computer systems do the most complicated jobs. Newer techniques have come in now that are basically combinations of all the pre-existing techniques. Deep Neural Networks research has become easier due to all the combinations of pre-existing techniques.
The DNN research is fast expanding due to the rapidly working computers and the contribution of the researchers who are constantly contributing to this process. Machine Learning, however, is still far from the efficiency of human performance. Human guidance is still needed by many computer systems even after the algorithms are provided. The major organizations are looking forward to blending the intelligence of human beings and the artificial intelligence of machine learning. This is taking the performance of the computers to an outstanding level of accuracy.
Difference Between Data Science vs Machine Learning
|Data Science||Machine Learning|
|Data Science generally deals with structured or unstructured data.||Machine Learning deals with structured algorithms.|
|Skills of a good data expert are basic computer knowledge, statistical knowledge, and data knowledge.||Skills of a good Machine learning expert are Data wrangling knowledge, grasp on PIG or HIVE, knowledge of mathematical statistics.|
|Data Science does not have a relation to artificial intelligence.||Machine learning is done on a computer system to enhance artificial intelligence.|
|Data experts are needed to incorporate the works of Data Science.||Machine Learning minimizes the need for live agents for a number of works.|