let' talk about an inconvenient truth of data scienceand that' that you spend most of your time actually just cleaning and preparing your dataand actually relatively little of it analyzing it and trying out new algorithms it' not quite as glamorous as people might make it out to be all the time butthis is an extremely important thing to pay attention to there are lot of different things that you might find in raw data data that comes in to youjust raw datais going to be very dirtyit' going to be polluted in many different ways if you don' deal with it it' going to skew your resultsand it will ultimately end up in your business making the wrong decisions if it comes back that you made mistake where you ingested bunch of bad data and didn' account for itdidn' clean that data upand what you told your business was to do something based on those results that later turn out to be completely wrongyou're going to be in lot of troublesopay attentionthere are lot of different kinds of problems and data that you need to watch out foroutliersso maybe you have people that are behaving kind of strangely in your dataand when you dig into themthey turn out to be data you shouldn' be looking at the in first place good example would be if you're looking at web log dataand you see one session id that keeps coming back overand overand over againand it keeps doing something at ridiculous rate that human could never do what you're probably seeing there is robota script that' being run somewhere to actually scrape your website it might even be some sort of malicious attack but at any rateyou don' want that behavior data informing your models that are meant to predict the behavior of real human beings using your website sowatching for outliers is one way to identify types of data that you might want to strip out of your model when you're building it missing datawhat do you do when data' just not theregoing back to the example of web logyou might have referrer in that lineor you might not what do you do if it' not theredo you create new classification for missingor not specifiedor do you throw that line out entirelyyou have to think about what the right thing to do is there malicious datathere might be people trying to game your systemthere might be people trying to cheat the systemand you don' want those people getting away with it let' say you're making recommender system there could be people out there trying to fabricate behavior data in order to promote their new itemrightsoyou need to be on the lookout for that sort of thingand make sure that you're identifying the shilling attacksor other types of attacks on your input dataand filtering them out from results and don' let them win