Welcome to the second week of the program. This week we're focusing on the Open web, a different type of alternative data. We consider websites, personnel website or corporate own website or even blogs as part of the universe of the Open Web. References such as Wikipedia or Investopedia in the financing economic sense are part of the Open Web universe as well, and even more formal dictionaries such as the Oxford Dictionary of Banking and Finance could be considered part of the Open Web from which we can draw information, analyze the data, and derive insights. Government is another source of information that we can extract from the Open Web. Take the Department of Defense as an example. They actually publish on the website every transaction and every contract they signed every given day. So imagine how much we can learn about the economy, about different companies, vendors, and contractor that either just won a contract or actually lost a contract. Treasury and the census are other two examples of government providing information through the website which we can tap into through the Open Web. So what we can learn from all of these? Well, we can understand trends in the economy, we can understand trends in employment, we can understand the hiring processes of different companies through the information they put on the websites, we can measure productions across many different sectors and within different industries. We can also measure connection between firms based on measures of similarity for example drawn from information collected from the Open Web. But to arrive to this insight, there are few hurdles we have to overcome. First, we need to identify the sources from which we intend to extract information. Then we actually need to go and extract the information and bring the data into our systems. Because most of the data in the Open Web is provided in the textual form, we need to provide skills and tools to analyze text. Then once this analysis is done, we can actually try to think about financial application that will benefit from the information that we extracted and analyzed through the Open Web. This is exactly what we're planning to do this week. In the first few session, we'll focus on textural analysis. We learn the techniques that allow us to analyze texts and bring it to a form which is usable by financial applications. Then we'll go through a series of lab session that will allow us to learn the tools by which we can extract information from the web mostly through web scraping, we'll apply in a different lab the textual analysis techniques on the data we extracted, and finally, we'll develop alternative measures of industry classification using the textual content that we extracted. We will finish the week with introducing an application that use similarity measures on text to predict returns.