the data science process data owner the result is data in its raw formwhich probably needs polishing and transformation before it becomes usable now that you have the raw datait' time to prepare it this includes transforming the data from raw form into data that' directly usable in your models to achieve thisyou'll detect and correct different kinds of errors in the datacombine data from different data sourcesand transform it if you have successfully completed this stepyou can progress to data visualization and modeling the fourth step is data exploration the goal of this step is to gain deep understanding of the data you'll look for patternscorrelationsand deviations based on visual and descriptive techniques the insights you gain from this phase will enable you to start modeling finallywe get to the sexiest partmodel building (often referred to as "data modelingthroughout this bookit is now that you attempt to gain the insights or make the predictions stated in your project charter now is the time to bring out the heavy gunsbut remember research has taught us that often (but not alwaysa combination of simple models tends to outperform one complicated model if you've done this phase rightyou're almost done the last step of the data science model is presenting your results and automating the analysisif needed one goal of project is to change process and/or make better decisions you may still need to convince the business that your findings will indeed change the business process as expected this is where you can shine in your influencer role the importance of this step is more apparent in projects on strategic and tactical level certain projects require you to perform the business process over and over againso automating the project will save time in reality you won' progress in linear way from step to step often you'll regress and iterate between the different phases following these six steps pays off in terms of higher project success ratio and increased impact of research results this process ensures you have well-defined research plana good understanding of the business questionand clear deliverables before you even start looking at data the first steps of your process focus on getting high-quality data as input for your models this way your models will perform better later on in data science there' well-known sayinggarbage in equals garbage out another benefit of following structured approach is that you work more in prototype mode while you search for the best model when building prototypeyou'll probably try multiple models and won' focus heavily on issues such as program speed or writing code against standards this allows you to focus on bringing business value instead not every project is initiated by the business itself insights learned during analysis or the arrival of new data can spawn new projects when the data science team generates an ideawork has already been done to make proposition and find business sponsor