Difference Between Data Science and Machine learning
Difference between Data Science and Machine Learning
The information source of any industry, work, sector is Data and the importance of professional cataloging in the field of data is growing at a prolific rate. It is a prediction of IBM that by the end of the year 2020, the number of data professional jobs will increase by 3,64,000. At this rate, we can only imagine the swiftness of this fast-growing domain.
Now, to elaborate on the matrix of this field, let us look at the distinction between data Sciences and Machine Learning in detail. To understand the dynamics in easier terms, Data Science is an umbrella term which includes subsets like machine learning. Thus, in an organization a Machine Learning Engineer will be hired I the Data Science learning team. The spectrum of data Science includes in itself algorithms and statistics and undertake the process of entire data methodology. Machine learning will also use algorithms but these algorithms will be to use data and learn from these data at the same time making future predictions. Data Sciences, on the other hand, is used to handle programs like Big Data which will include in it the process of data cleaning, preparation and analysis.
|Machine Learning||Data Science|
|Knowledge of Computers important||Knowledge of Machine Learning Important|
|Data evaluation skills important||Analytical Skills Important|
|Knowledge of probability and statistics important||Experience in SOL Database coding|
|Knowledge of Programming skills Important||Knowledge of Python, SAS , R and Scala important|
The work of a data Scientists is to cleanse, prepare and analyse data thus they handle big concepts like Big data. So somebody who is data Scientist will apply the knowledge of Machine learning invariably. The idea is to understand data from the point of view of the industry and its business and then lead to accurate predictions to increase profit and reach accurate business decisions. Hence, here the most important skill that is required from the candidate is Analysis.
It is a combination of two things – Statistical analysis and Predictive analysis. It is a mode of using algorithms to use data and learn from those data so that forecast about the future can be determined. A good example of machine learning is “Netflix” in this based on the choice of programs and the latest shoes and movies watched by a particular use there is a recommended list of series and movies that is offered to the individual. This prediction has been made possible after analyzing the interest and history of usage of the user. For this area, the most important skill that is required from the individual is probability knowledge and programming skills.
Thus, now we can clearly analyse the two fields and establish that the main difference between the data sciences and machine learning is although Data Science also requires one to focus on the algorithms and statistics just like Machine learning there is also a data processing methodology that is required in data Sciences Thereby making it a much bigger term.