table list of open-data providers that should get you started open data site description data gov the home of the us government' open data the home of the european commission' open data freebase org an open database that retrieves its information from sites like wikipediamusicbrainsand the sec archive data worldbank org open data initiative from the world bank aiddata org open data for international development open fda gov open data from the us food and drug administration do data quality checks now to prevent problems later expect to spend good portion of your project time doing data correction and cleansingsometimes up to the retrieval of data is the first time you'll inspect the data in the data science process most of the errors you'll encounter during the datagathering phase are easy to spotbut being too careless will make you spend many hours solving data issues that could have been prevented during data import you'll investigate the data during the importdata preparationand exploratory phases the difference is in the goal and the depth of the investigation during data retrievalyou check to see if the data is equal to the data in the source document and look to see if you have the right data types this shouldn' take too longwhen you have enough evidence that the data is similar to the data you find in the source documentyou stop with data preparationyou do more elaborate check if you did good job during the previous phasethe errors you find now are also present in the source document the focus is on the content of the variablesyou want to get rid of typos and other data entry errors and bring the data to common standard among the data sets for exampleyou might correct usq to usa and united kingdom to uk during the exploratory phase your focus shifts to what you can learn from the data now you assume the data to be clean and look at the statistical properties such as distributionscorrelationsand outliers you'll often iterate over these phases for instancewhen you discover outliers in the exploratory phasethey can point to data entry error now that you understand how the quality of the data is improved during the processwe'll look deeper into the data preparation step step cleansingintegratingand transforming data the data received from the data retrieval phase is likely to be " diamond in the rough your task now is to sanitize and prepare it for use in the modeling and reporting phase doing so is tremendously important because your models will perform better and you'll lose less time trying to fix strange output it can' be mentioned nearly enough timesgarbage in equals garbage out your model needs the data in specific