 A CSV file is a well-known and widely used text format for data exchange. CSV formats are the best used to represent sets or sequence of records in which each record has an identical list of fields. Now because CSV is a versatile, the CSV file format is supported by many software applications. By using a structure similar to that of spreadsheet, it also allows user to present information in a way that is easy to understand and share across applications including relational database management system. Okay, so let's consider a scenario where you are working on some machine learning application or you want some simple data-to-data analysis which requires some large CSV files. So now what to do in this situation? Well, in the beginning of this year, Google had launched the Dataset Search tool similar to regular Google Search. This Dataset Search tool helps you to locate and provides access to publicly available datasets. It has more than 25 million data sources from repository across the web like from government data to consumer sales data and many more. So say for example, if I search NFL football or say player states, then you will get the result something like this. And from here, you can further filter your results sets. Say for example, I want the results only in text or documents, etc. So if I scroll down, then you can see the different different sources from which you can get the NFL player states. So say for example, if I select this one, then here you can see the detailed information of this Dataset. Say for example, this is the last updated date of this Dataset. This is the author of this Dataset. And when you click on this link, you will be redirected to the source of the Dataset. So in this case, it is Kegel. And if I scroll down, then you can see the different different types of CSV file related to NFL. And say for example, if I select this file, then here you can see that the size of the file. And here is the kind of statistics which you will get in the CSV file. For example, this CSV file content 5263 unique values of player ID and same way for player name. And from this link, you can download this CSV file. So when you click on this option, it will ask you to log in, but you can select the last option, skip and continue to download. And it will ask to save the file. So same way you can also refer the other data sources which are listed over here. So this Google's Datasets tool search for all the Datasets that are available in the market. So what are the other places where you can find large dataset in case if you are associated with some domains like data science, data mining, AI or machine learning related stuff. Well, for that, you can find one interesting article on Bitescout website. So let me open that article. Alright, so big dataset providers are now growing exponentially every day. In this article, Bitescout team had done a very fantastic job to evaluate a variety of datasets and big data providers ideal for machine learning and data mining research projects. In this article, you will also see a code sample in Python which shows how to use one of the most popular big data host. So here is the top 50 big data provider list. If you dig deeper in this article, you can see one remarkable dataset from each provider. Keep in mind that most of these providers host thousands of datasets. All of the items which you can see here are currently maintained and updated. Say for example, let me scroll up. If I click on this kegel link, then here you can see this brief introduction of OpenAQ dataset. It means if you are interested in global air pollution measurement or climate change forecasting, then this OpenAQ dataset is for you. So if I open kegel website, which we have already seen earlier, then in the homepage, you can find something like this. And in the top, when you click on this dataset links, so here you can see the variety of datasets like this recent US election, COVID-19 related datasets, etc. And all these datasets are publicly available. So in short, kegel is one of the very biggest resources that you can find online to find out any dataset that you require. So large CSV files are very helpful in data analysis and data mining related applications. Many libraries of different programming languages can handle large CSV files easily and most of them provide some way to specify the field delimiters, character encoding, coding conventions, debt formats, etc. And with this we have reached the end of the course. Hope you have enjoyed this course and stay tuned with us to get more updates. So till then keep on learning and I will see you next time.