Hi,i am writing blogs for our platform House of Bots on Artificial Intelligence, Machine Learning, Chatbots, Automation etc after completing my MBA degree.
...
Hi,i am writing blogs for our platform House of Bots on Artificial Intelligence, Machine Learning, Chatbots, Automation etc after completing my MBA degree.
Data science has revolutionized the technological world. We all are aware of the term data science that there is definitely something in data science that sets it apart and make being a data scientist one of the best jobs of the twenty-first century.
Importance of Data Science:
Data science can be used by companies to manage and extract various data from large pools of information. This can help companies produce better products and services for their customers by consistently analyzing their feedbacks and reviews. This helps various engineering and business firms to improve themselves and make various business decisions.
Also, did you know that data science can help you predict what the next scene of a movie or a drama will be, or how people from different cultures and economic backgrounds will respond to different things, or even the future? Is it not surprising enough? Yes, it is indeed.
Predictive Casual Analytics:
Now, this is the most important kind of analysis in data science. Suppose you want to predict an outcome in the future. For example, if you are lending someone money, and you want to know if they will pay back your money on time or not just to be sure, you can devise a model based on predictive casual analysis by which you can check their previous money repayment records to know if they have a history of delaying the repayment of any loan. Hence, you can know whether to lend them your valuable money or not.
Prescriptive Analytics:
This model can be used to make something which can intelligently take decisions on its own. For example, there are certain cases where you want to know whether or not to do something. During such cases, you can take help from such a model. Obviously, a computer cannot think on its own. Therefore, some data is to be fed to the machine beforehand in order to make it think in a similar way and take appropriate decision. The best example of such a model is Google Self Driving Car. As the name itself implies, such a car can take decisions on its own as to when to turn and when to not, and whether to turn left or right just by knowing the location of the place via GPS where the passenger wants to go.
Machine Learning Used for Prediction:
Suppose you want to predict future trends of something, then this model can come in handy. This model is extensively used by various companies across the world to study past trends and then predict the Hadfuture accordingly.
Machine Learning for Predicting Patterns
Suppose that there is no particular parameter on which you have to extract out the data. Then, using this model, you can train a computer to look for various repetitive patterns in data and extract out something meaningful from it. This is also an extensively used model. Hence, the scope of data science is very vast and one can do a lot of research if one is really interested in it. It is up to the data scientist as to which discipline in the field of data science he wants to specialize in.
It has become a buzz word as far as today's Information Technology world is concerned. This happens with a lot of technologies which people start talking about as a jargon with no understanding of what is meant by the technology, what falls in its scope and so on. We shall undertake such discussions in a bit of detail. The confusion starts the very moment you speak of data science as part of today's technical scenario. It comes with its various components. Whenever you speak about the constituents of data science, you basically speak about big data. This is when you also talk of various jobs that form part of Data Science what really is a Data Scientist's role, what exactly is the Data Curator's role, what exactly id the Data Librarian's role and so forth. In today's scenario when you speak of it as a field within itself, it essentially deals with large chunks of data.