Congratulations on completing four out of five courses in the Excel to MySQL specialization. We know how hard you worked and energy and dedication you needed to get this far. You are now officially 80% data analysis unicorn. We hope that you are excited about the new skills you have learned and have already started putting them to good use. >> In this Capstone project, we are going to give you a chance to integrate many of the most important concepts and skills you've learned over the last four courses, and practice applying them to a new situations >> Step by step over the next few weeks. We're going to guide you to won a business data analyst project looks like from beginning to end. You'll take the role of a data analyst in a fictional property management firm watershed properties. You're going to be ask by the company executives provide the business recommendation based on your analysis and most specific business question >> You will start by eliciting information to define the expectations, constraints, and time frame of the project. Then you will query a database to learn what data are available to you and to extract the data that would be most useful for your analysis. >> Then you will build a predictive model in Excel >> integrate that predictive model in to a financial model about how much money watershed could make and test how robust your financial model is to changes in your assumptions. Using sensitivity analysis in both Excel and in Tableau. Finally you craft a presentation that uses key results. >> To convince the watershed executives to adopt your business recommendation. By the end you should feel confidence in your ability to develop specific actionable recommendations from raw business data, and in your ability to communicate those recommendations in a compelling manner. >> Let's talk a little bit about the project you are going to working on for the next few weeks. If you don't know anything about real state, you are about to learn. As mentioned, you are going to take the role of a business data analyst for a fictional property management company called Watershed Properties. Property managers take care of the daily operations in rental houses and apartment, and either can't or don't want to take care of those details himself. In return for getting paid a percent of the rent collected. A company like Watershed, it's hard to do things like recommend rental rates, find renters, collect rent, and manage your team maintenance pairs. >> Thus far, Watershed has rented all of its properties on a long-term basis with yearlong leases >> However recently watershed has started paying attention to all the buzz about websites like Airbnb, VRBO, HomeAway and FlipKey and they're making it possible to rent out residential properties to many different guest for short intervals more like hotel rooms. Watershed has heard rumors that this business model can be very lucrative. They have recently found out that there is an opportunity to enter the short term rental market using the properties of one of their major clients and they want your recommendation about whether they should do so or not. >> There are a lot of interesting factors to consider when thinking about the short term rental market. >> First, although it is true that renters are willing to pay more per night for a short term rental than a long term rental, short term rentals also cost much more to maintain than long term rentals and have lower occupancy rates than long term rental properties overall. >> Second, perhaps even more than other areas of the real estate field, the short term rental market is an area where the ability to analyse data offers a big competitive advantage. There may be opportunity to make substantially more profit on short term rental properties than other owners do. If you can create and apply a model that forecasts rental prices to optimize occupancy rates and maximize overall revenues. >> Over the next few weeks you are going to figure out how to integrate these factors into an analysis that will help you decide whether you should advise Watershed to get into the short term rental market or instead stick to the long term rentals that you already know how to manage. >> You're going to start your project just like any data analyst would, through doing some background research and asking questions during elicitation. Your goal during this first week of the project should be to clarify, what exactly is it that I am supposed to do? What information do I need to do this correctly, given the time and resource constraints on my project? Once I have that information, how am I going to analyze it as efficiently and effectively as possible? >> You may be reluctant to ask questions, and may be eager to just get started with the data analysis. Just remember what we talked about in the data visualization and communication with Tableau course. One of the major reasons big data analysis projects fail, is that analysts don't ask or listen to their stakeholders to find out what the stakeholders want, or would be willing to adopt. >> Use the first week as an opportunity to practice extracting the critical pieces of information out of what people write or say. You'll see that it's actually a challenging skill to master, you have to listen very carefully and be flexible in your thinking. You also have to meticulous in documenting the information you've gathered so that you can account for everything you need in order to get a correct answer, and also so that you can communicate your key assumptions to your stakeholders later on. >> Once you have used materials available to you to determine the expectations and constraints of your project, at the end of the week you will confirm your objectives with your project manager. Then, the following week, you will be able to start working closely with your data. You will find out what data watershed has and we'll come up with the plan for how to use those data to determine whether watershed should enter the short term rental market. >> Once you have the data in hand, you will use Excel. First, to build predictive models of rents and occupancy rates from which you can estimate future rental revenues. Then, to build detailed financial models, to determine whether this new business can be profitable and cash flow positive. You will also be able to determine how sensitive your financial model is to changes in input assumptions. >> After the Excel portion of the project, you will create a dashboard in Tableau to extend and visualize the sensitivity analysis around your model assumptions. Finally, you will create a compelling presentation that effectively communicates your recommendation about how to proceed based on your data findings. It is our hope that your work product will be a persuasive demonstration of your new skills that you can show to others, including potential employers. >> And don't worry, we'll be with you all along the way to help point you in the right direction. We also really hope that you will help each other out in the discussion forums, because we're all in this together. We can't wait to see what you come up with. >> Get ready to be a watershed data analyst. [BLANK AUDIO]
