5 Advanced techniques of data cleaning & pre processing using R
If you are working with analytics or data science, you probably know that data cleaning is by far the most tedious and time consuming task of all.
We spend about 70 percent of the total time on data preparation.
If we want to be more efficient in data processing then we want to learn those advanced techniques to understand how to best prepare our dataset and how to be most efficient of going out to learn how to do it quicker.
We will also learn how to streamline the process of data preparation so we spend less time there and we will make less errors because we know what we are doing.
We also would want to investigate the potential threats that we will face, and prepare ourselves for the factor TARP and in this session we're going to be talking a lot about locating missing data how to find it and find not only missing data but also how to replace missing values.
We also have a few methods of placing missing data.
One of the more advanced ones will be the main method and the immediate imputation method.
These are very powerful techniques that are used in our programming for different types of analytics. So those are very important methods to know.
And if you are someone who is working with factor for the first time then you are very likely to fool yourself so it's always better to know it in advance.
And personally I fall into that trap lots of times and I know how it feels and how long it takes to figure out where the error is.
So when you watch it and after that you will know how to deal with Factor variables when you're performing those conversion.
That's what we're talking about.
So very exciting session ahead.
Lots of different tips hack's knowledge is going to be thrown at you so be prepared for the cold and learn a lot of new stuff.