You gained a solid foundation in SAS programming, and now it's time to answer real world data questions by applying your knowledge of the SAS programming process. In this overview, I'm going to explain the three key pieces of information that you need to begin. First, I'll describe the scenario you are in, and the business problem you are trying to solve. Then I'll quickly describe the data you'll be using and list the columns you will be working with. Lastly, I will identify each stage of the SAS programming process and how you apply your knowledge of this process to analyze United States TSA claims data. For this case study, suppose you've been in a new job for about six months. You recently completed a SAS programming course so you can start creating basic reports and maintaining SAS programs at your company. Now your manager has decided to give you your first SAS programming project. You've been asked to prepare and analyze claims data from the Transportation Security Administration, or TSA. The TSA is an agency of the US Department of Homeland Security that has authority over the security of the traveling public. Claims are filed if travelers are injured or their property is lost or damaged during the screening process at an airport. You have data for claims filed between 2002 and 2017. You don't really know what you'll find in the data yet, but you know eventually you will need to analyze and report on the overall data and also by any specified state. Now let's talk about the data you'll be using for this project. The case study data is in a CSV file named TSA Claims 2002 to 2017. This file was created from publicly available data from the TSA and the Federal Aviation Administration, or FAA. The TSA data has information about claims and the FAA data has information about USA airport facilities. The case study data was created by concatenating individual TSA airport claims data, removing some extra columns, and then joining the concatenated TSA claims data with the FAA airport facilities data. The TSA Claims 2002 to 2017 CSV file has 14 columns and over 220,000 rows. Let's look at the columns you'll be working with in the data. The Claim_Number column has a number for each claim. Some claims can have duplicate claim numbers, but different information for each claim. Those claims are considered valid for this case study. Incident_Date and Date_Received columns have the date the incident occurred and the date the claim was filed. Claim_Type has a type of the claim. There are 14 valid claim types. The Claim_Site column has where the claim occurred. There are 8 valid values for claims site. The Disposition column has a final settlement of the claim. The Close_Amount column has dollar amount of the settlement. The Item_Category column has a type of items in the claim. The values in this column vary by year, so you won't work with this column in this case study. Airport_Code and Airport_Name columns have the code and the name where the incident occurred. The County, City, State, and Statename columns have the location of the airport. The State column has a two letter state code and Statename has the full state name. To complete this project, you follow the SAS programming process. Each step in the process has tasks for you to complete. Your first task is to import the TSA Claims 2002 to 2017 CSV file into SAS. Without the data, you can't continue the SAS programming process. Next, you need to explore the data. As you know, this is vital to understanding what you are working with and what you might need to fix. In this task, you examine your data and look for any issues that might need to be resolved. Remember this is real data, and real data usually has some inconsistencies. So be sure to look for any problems and validate the data against the data requirements you've been given. After you've accessed and explored the data, it's time to get to work. In this stage, you write SAS programs that use procedures, functions, conditional logic, WHERE statements, formats, and labels to prepare the data so that it meets the requirements you've been given. This stage typically takes the most time in the programming process, but it's important to properly prepare your data so you can analyze it and create accurate reports. Once the data is prepared, it's time to use it to answer the business questions you've been given. To answer these questions, you write programs to create reports, summary statistics, and a visualization. Once you've analyzed the data, it's time to export the results to a PDF report that you can share in a meeting with your manager. By the end of the case study, you have demonstrated your knowledge of the entire SAS programming process and applied it to a real world business problem. Now that you have an overview of what you'll be doing, the next step is to read the PDF document that gives you all the details. Read all the requirements for what you need to do, and then work on the project one step at a time. It's time to get to work.