Hello again. If you're watching this lecture, that means you made it to the end of the first course. Congratulations. In this course, you learned some new skills that helps you to go from data to some simple data products. Let's see what you learned. In week one, we introduced what data products were, what's the data to product pipeline, and we also looked at some real life data products that make impact on our everyday lives. Following this in week two, we started looking at the basics of data ingestion. So how did we deal with structured file formats like JSON and CSV. We also described some common datasets in particular from Amazon and Yelp, we're going to use throughout the specialization to develop working data products. This is an example of the JSON structure data that we read from Yelp. In week three, we start looking at exploratory data analysis. So how do we take those datasets we've ingested, filter them, clean them to extract meaningful information, extract some simple statistics from those data sets, and also deal with the structured and semi-structured data types that we see. For example text, string, time, and date processing. Finally in week four, we covered data visualization. Like how we generate simple plots in matplotlib. How can we actually crawl and maybe collection in your datasets so you can really go end-to-end from this collection to visualization pipeline, and finally how do we actually develop data product strategies to build data products in the real world. So based on what we've seen in these first four week, what you should now be able to do is to collect and ingest structured datasets you might have collected from the web, for example in CSV or JSON format using Python. You should then be able to extract some statistics from those datasets including any text, time, or date data those datasets might include, and finally you should be able to visualize those datasets by creating simple plots we can visualize the main trends with data sets might exhibit. So just to give us a brief introduction we will cover in the next course. Having seen how to collect, ,and visualized data, next we're to how do we apply machine learning systems to those datasets. How can we actually build predictive analytics tools in particular look at regression and classification techniques, and we'll show how it can actually extract meaningful features that can be used to make predictions effectively from those datasets.