Make a summary of this including tools and what they are used for: If your goal is to make a spaghetti dinner but you don't have the right ingredients to make the dish, then your success will be compromised. Think of this section of the data science methodology as cooking with data. Each step is critical in making the meal. So, if the problem that needs to be resolved is the recipe, so to speak, and data is an ingredient, then the data scientist needs to identify: which ingredients are required, how to source or to collect them, how to understand or work with them, and how to prepare the data to meet the desired outcome. Building on the understanding of the problem at hand, and then using the analytical approach selected, the Data Scientist is ready to get started. Now let's look at some examples of the data requirements within the data science methodology. Prior to undertaking the data collection and data preparation stages of the methodology, it's vital to define the data requirements for decision-tree classification. This includes identifying the necessary data content, formats and sources for initial data collection. So now, let's look at the case study related to applying "Data Requirements". In the case study, the first task was to define the data requirements for the decision tree classification approach that was selected. This included selecting a suitable patient cohort from the health insurance providers member base. In order to compile the complete clinical histories, three criteria were identified for inclusion in the cohort. First, a patient needed to be admitted as in-patient within the provider service area, so they'd have access to the necessary information. Second, they focused on patients with a primary diagnosis of congestive heart failure during one full year. Third, a patient must have had continuous enrollment for at least six months, prior to the primary admission for congestive heart failure, so that complete medical history could be compiled. Congestive heart failure patients who also had been diagnosed as having other significant medical conditions, were excluded from the cohort because those conditions would cause higher-than-average re-admission rates and, thus, could skew the results. Then the content, format, and representations of the data needed for decision tree classification were defined. This modeling technique requires one record per patient, with columns representing the variables in the model. To model the readmission outcome, there needed to be data covering all aspects of the patient's clinical history. This content would include admissions, primary, secondary, and tertiary diagnoses, procedures, prescriptions, and other services provided either during hospitalization or throughout patient/doctor visits. Thus, a particular patient could have thousands of records, representing all their related attributes. To get to the one record per patient format, the data scientists rolled up the transactional records to the patient level, creating a number of new variables to represent that information. This was a job for the data preparation stage, so thinking ahead and anticipating subsequent stages is important.