This is one of my favorite modules. What we're going to do here is compare common data exploration techniques, and focus on writing some good SQL, and BigQuery on our own course Dataset. Now that you're a little bit more familiar with the dataset that we're going to be exploring which is that IRS charity Dataset, let's talk about your different options for how you can actually explore that data. So as a data analyst, you're not necessarily limited to just using SQL instead of the BigQuery WEB UI. There's also some other pretty cool data preparation tools, like Cloud Data Prep, that we're going to introduce you to and later on. And last but not the least, you can actually explore your data visually using a Visualization Tool, like Google Data Studio, or Tablo or Looker, or another one of those tools. So, it's not necessarily a linear process going from writing SQL, and BigQuery processing in, and then visualizing it, you can use each of these different tools at any point that you want, and a lot of it is a matter of preference, depending upon who you talk to. So, the first thing that we're going to explore first is using the SQL approach, and using the BigQuery Web UI. Why? Because SQL is a very good skill to have, almost an imperative skill to have as a data analyst, and it's one of the fastest ways you can interact with data behind the scenes inside of BigQuery and it's fun. So, what do you actually have to do before you write this query on your dataset? The first is often the hardest part is thinking up of a question, or some kind of unknown, or anything that just kind of gives you interest about the dataset. So, here you could look for something as simple as, well just give me the top revenue, or the lowest revenue for these organizations in this Dataset. But, coming up with these really complex questions, can often be the most challenging, or difficult part right? It's like a blank canvas when you first look at the dataset, and then you have to determine, Well okay, well, where do we start? As you'll see a little bit later on when we get into a Cloud Data Prep, having some basic statistics of frequency values, and how many meet data type constraints, and whether or not you have missing values, can be a great first start. But, for now we're going to start with a blank canvas, which is just the dataset schema, and then throw some SQL on it. Second, of course is accessing that dataset, this presumes that you already have loaded your data into BigQuery and again here we're using the public dataset, and last but not the least, as you're going to get very familiar with in this talk is writing the SQL that is going to query fields, and rows that you actually want to return as part of your question, and converting it from your question all the way on the left, to interpreting it as part of like SQL is a skill that you going to master, as you get more and more familiar with basic, intermediate, and advanced SQL. A two-second background on SQL. So I call it Sequel or SQL. It is that Structured Query Language, it's been around since the 80's. There is a national standard language library for SQL, called On C 2011 SQL, and BigQuery standard SQL follows those standards, and writing the query in standard SQL Mode gives you a lot of those performance advantages that we'll talk about in later courses. It is Pseudo English. Pseu, using things like Select, which basically means, give me these columns from this dataset. Give me the names of these charities, give me the revenue that they have, from this particular dataset table, and then do some kind of manipulation on it. So, if you hear Order by is synonymous with sorting, sorting it alphabetically, sort it highest to lowest. So, you get very familiar with these dark blue all capitalized words, called the SQL Clauses, as well as the order in which you place them in your queries, and that's what we're going to review for a large part.
