There has always been lot of confusion whether data science refers to an area of science or computer science and what about machine learning is simply referred to as machine learning or artificial intelligence. However, if you dig deeper into both of them then you are bound to discover some major difference between science data definition and machine learning definition. Data science simply refers to the process of obtaining large amounts of data from diverse sources and analyzing these data. Machine learning on the other hand is concerned with using computers for the purpose of conducting different types of experiments, evaluating the results and making statistical comparisons between different data sets obtained from various sources. Hence, one can say that data science deals with the analysis and interpretation of data and machine learning deals with the actual implementation of the mathematical and conceptual models created for the purpose of such analysis.

The main difference between data science and machine learning is that data scientists mostly deal with the problem or opportunity to machine learning experts are mostly analytically oriented. This means that they mainly depend on the past results rather than on making any intelligent or creative efforts towards the future. They are also aware of the importance of statistical calculation, probability, efficiency etc in data analysis. Data science basically deals with the problems in generating, organizing, and storing data. Machine learning on the other hand mainly relies on the training of computers to solve problems which currently lie in front of them and they are yet to create. They are only capable of doing the tasks in the short run only where as data scientists are capable of creating and maintaining long term relationships that will ultimately increase the productivity of any business.

The biggest similarity between machine learning engineers and data scientists is that both are academically trained, but they have divergent interests. Although both are smart and talented, the differences lie in their approach to a problem. Machine learning engineers are primarily concerned with how an algorithm can solve a specific problem, whereas data scientists mostly weigh benefits and costs in an effort to come out with a solution for a particular problem. They also may use statistical methods in solving problems. In both cases, they should be capable of discerning the relevant data from irrelevant data and analyzing them in order to determine an algorithm that can efficiently solve a problem.

Another striking similarity between these two fields is that both involve programming of computers in a structured manner. This however does not imply that the difference between data science and machine learning engineers is merely a matter of programming. Machines can only do things efficiently with predefined set of instructions and procedures, whereas a human being has the capacity to think creatively and thus generate programs for a machine that can solve problems. Similarly, humans can easily convert their thoughts into programs and use them to solve a problem.

Some important differences between data science and machine learning engineers also pertain to the responsibilities that each one has to perform. Data scientists directly deal with the analytical part while machine learning engineers mainly deal with the implementation part. The Machine Learning Engineers is responsible for ensuring that the algorithm is implemented properly. They also ensure that the system is compatible with the specific hardware and software needed for its execution.

The biggest difference between data science and machine learning lies in the fact that data scientists are not directly involved in the designing process whereas machine learning engineers are usually involved in the initial design of the machine. A data scientist may have a vision for the product or service that he is designing and is able to translate this into a real product. In contrast, machine learning engineers design the machine that is used to implement the desired program. Data scientists are usually involved in data analysis and they derive the mathematical or graphical data required by the designers in order to create a product.

Machine learning algorithms provide useful insights by reducing the Risks associated with un-anticipated results. However, when using data science techniques, it is important for the programmer to ensure that he has written the necessary machine learning algorithm that meets the targeted use. It is also important that the algorithm is reusable and able to generate relevant useful insights on a continual basis.

Similar to machine learning, data modeling incorporates a range of methods such as principal components analysis, neural networks, artificial intelligence, decision trees and many more. However, the biggest difference between data modeling and data science lies in the responsibility of each to provide a useful insight on a continuous basis. In data modeling, programmers can ask the machine to generate relevant insights while in data science, the programmer is the one who asks the machine to generate relevant insights. Data scientists usually have to meet the primary responsibility of improving the predictive power of algorithms. However, they also have the responsibility of improving the general accuracy and efficiency of any machine learning algorithm. As such, data science is more involved in generating new insights than it is in finding new applications for existing algorithms.