We all know what data is. It’s the information we exchange, the data we process and convert to something meaningful. Data science is performing meaningful operations that make it function in different ways. According to recent estimations, the demand for data science is expected to grow to 40%. In India, there’s only 10% working in it. There is a need for more professionals in this industry, which is why we are focusing our attention on this topic. We might be struggling to manage small amounts of data at times. We might not be able to focus on our data carefully due to our busy schedules. Figuring out how the data must be managed takes a lot of time which is why we will now discuss a basic methodology to work on data.
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1. Frame your question wisely: let me give you an example. Consider the voting system database. You may want to extract 2 particular cities out of the national database system. When you need to do this, there are two possible ways to accomplish the task.
What is the voting ratio between the western coast and the eastern coast?
What is the ratio in Maharashtra and Orissa?
Both of these questions, request the same kind of data, but when you have to know about one particular place or a category in general, you must specify the exact category from which you want the data to be collected. The first question has to move through the entire east and west coasts which takes time and is quite a load on a CPU. Whereas the second question focuses on the states of Maharashtra and Orissa. This allows for faster processing and reduces time complexity.
2. Read your data clearly: after we extract data, we cannot be certain that the data is in the format we need. It is usually in a messy format, with a lot of junk, which is probably not what is required. So, even before we check on the actual data we’re looking for, we need to perform some data cleaning. This process removes the junk and then you can actually provide a format which will allow you to view the data as needed.