There are just tremendous amounts of data out in the world, but a whole lot of it stays in a format that is not easy to use. It is part the role of the data scientist who is involved to ensure that the data is formatted very nicely and conforms exactly to the agreed upon sets of rules.
Cleaning of Data
As an instance, take a CSV file where each of the rows signifies the finances as they relate to a franchise of a fast food chain. The file may have specific columns for the state, city and the number of sold burgers over the past one year. But instead of having all this data in one single document (which would have made our lives much simpler, right?), it actually comes spread in many different data stores (of files), which now have to be combined together. Now, let us assume that it may be easy to do this part. Still the hardest part is going to be to make sure that the combination that results makes some sort of sense.
There will typically be inconsistencies in formatting, and what you will find is that somewhere in the dataset, there is a row floating around which shows California as the number of burgers sold while the state shows up as 30,000. Cleaning all this data is all about finding such problem and fixing them, and then making sure that they will eventually be fixed in an automated manner in the near future. Now as a thrown in bonus, all of this downstream work that you will do from here on can never be better than the kind of data you have assembled.
Analysis of Data
This is all that sort of boring and time taking work which people mostly put Excel to use for, but just a lot more juiced up dramatically. People working in the area of data science will usually have to work with such data sets which are way too large for them to be possibly opened by a typical program that deals in spreadsheets, and sometimes may even be too big to work on even with a single computer.
Data Science training in hyderabad happens to be the area of visualization (tables are not fit for human consumption). This is exactly where you end up making a lot of plots corresponding to the data as you are attempting to get an understanding it (plotting also happens to be another area where the spreadsheets just lag behind). Using this particular process, the data scientist will try to create a story, which will help to explain the dataset in such a way which will be easier in communicating and easy do for someone taking action on it. This can sometimes be very simple, like trying to figure out what event of property signals when some new users get converted in users who can be called long-term, or it could be something which is a lot more complex, like to find out when someone is very slowly scamming you for tons of money.
Now that you understand the kinds of data crunching a data scientist needs to do, you may understand why this job is much respected. The whole company is looking at you for answers and you hold a very important position in the organization. You will need some focused training if you really want to be successful. We recommend you start from this data science course.
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