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In this video, you're going to learn the basics of SQL querying in just 15 minutes.

And by the time we're finished, you'll be wondering what all the fuss was about.

Are you ready?

Then let's get started.

Hello and welcome to Vitamin BI, bringing you business intelligence for beginners and beyond.

On this channel I help you do more with data, so if you're new here consider subscribing.

Right, so as I said in my intro I'm going to teach you the basics of SQL querying,

the essentials that you'll need to know if you're considering becoming a business intelligence analyst.

Obviously we can only go so far in 15 minutes, but I promise you

that by the time we're finished

you'll be able to understand quite a lot of what you might be called upon to do.

We'll be focusing specifically on querying data, not creating or deleting databases,

tables or data, just selecting data from tables that we want to return for our specific needs.

Before we get started, let me give you a little context.

You might be wondering why you need to learn SQL with BI tools becoming more and more sophisticated.

Can't they write the SQL queries for me the answer to

that is yes and no yes

when it comes to requesting data for different charts graphs and tables

that you might want to present in dashboards in these cases

the bi tool will have some kind of graphical interface to be able to drag

and drop fields to return data but no in

that in a lot of cases you'll need to write sql queries to pre -aggregate filter

and select only the data necessary for the project that you're working on.

These are often called views and you create views mainly

because it's more efficient than connecting to all data when you only need some of it.

So you could have a table that contains millions of rows of data

but by creating a view of it your BI tool will

only need to work with a few thousand making analysis and presenting results much faster.

So you see, knowing how to query with SQL is a useful skill to have.

Let's jump onto my computer and get started.

So today we're working with a SQLite database that I found online, containing data compiled from the FIFA video game by EA Sports.

So soccer stats.

I'll leave a link to the database in the description if you want to play around with it too.

In terms of the tool I'm using to query the database?

It's called NaviCat.

It's the premium version that allows you to connect to and query pretty much any relational database.

But they also have a version for specific RDBMS like MySQL, SQL Server, etc. Link also in the description.

Here we're in the NaviCat interface.

On the left we can see that I'm connected to the SoccerDB connection

that I've created and in that connection is a database called main and in that database we have various tables country,

league, match, player etc. We're going to be working mainly with the player table.

If I double click it we'll see the data contained within that table.

We've got three different IDs, player name, birthday, height and weight.

What I want to do is write SQL queries

and ask the database to return specific data from this table so I'll open a new query window.

Now when we're asking for data we're using what's called a select statement

so that's what we need to write first and

if we want to return the whole table in the result we do so with a star.

Then we specify the table that we want to select everything from.

In this case the table called player select star from player then we run the query

and the database returns all data from that table you may notice

that i've written everything in lowercase

because the sequel isn't case sensitive however i have the option to what's called beautify the sequel

and when i do you'll notice that select

and from have been capitalized although it's not obligatory this does actually make your queries easier to read.

It's not so important with such a small query like this one

but when you've got one with tens if not hundreds of lines of code it really does make life easier.

So we've selected all fields or columns and all rows from the table

but what if we only want to select specific fields?

Simple.

Instead of the star we just write out the field names

we want separated by commas like this player underscore name comma birthday I run this,

and those are the fields returned.

When we select specific fields, we can also rename them by creating aliases.

To do this, we use the as function.

So player name as name.

And we can see that update.

We could specify an alias containing a space, but to do this, we would need to put the name in quotes like this.

Full name.

Let's go back to all data

and look now at how we can ask the database to only return specific rows in the result.

To do this we use a WHERE clause with different operators.

For example, we could select only players with a weight of 190 pounds.

So we use the equals operator.

We could also select any player with a weight greater than 190 pounds or greater than or equal to 190.

You see, it's not that complicated.

And we've gone from over 11 ,000 records to just under 1 ,000 being returned.

We can also specify more than one condition by using either

and or or so weight greater than 190 and height greater than 190

so both conditions must be met if I change it to

or it means that either condition must be met in order for the row to be returned

so that's selecting rows based on the value in integer fields what about when it comes to text values?

Well, we can also use the equals operator to find any row where the text matches exactly with what we specify.

PlayerName equals Aaron Galindo.

But we could also use the like operator which achieves the same goal.

But what if we want to select just rows from all players called Aaron?

So basically the player name field starts with Aaron.

Here we can use a percent character after the text to look for, like so.

And we get all Arons returned.

If we wanted to find any player name that ends with Aaron, we put the percent character before.

Nothing that ends with Aaron.

We could put a percent before and after, which would basically mean any player name that contains Aaron.

And we've got Rolando Arons.

Finally, here's an example which puts the percent in the middle of text.

So here we'll return all rows where the player name starts with a and ends with n.

you can go even further with the like operator by using

underscore to represent single characters an example would be like t underscore m percent

so the underscore is asking for any rows where the player name starts with t

then any character between the t and the m followed by anything and run.

100

We've got Tamas, Tamir, Tim, Timmy, etc.

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There are also things called wildcards that allow you to go even further

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when specifying the rows you want to return but we won't go into those for this video.

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We will however finish looking at the where clause by talking about a couple more operators that you can use.

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For text fields we have in which allows us to specify multiple or statements, more simply.

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However, it can only be used for exact matches, so we can't use percents or underscores with it.

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So let's look for Ronaldo and Messi.

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Open brackets Cristiano Ronaldo, Lionel Messi.

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For integers, we can use the between operator.

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So I could say wear weight between 180 and 190.

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Finally we have the isNull and isNotNull.

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Null being empty.

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In this table we don't have any null values.

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But if I open up the match table we can see that there are lots in there.

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So select star from match where home player 1 is null. or is not null.

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There we go.

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Let's go back to our player table and see how we can sort results.

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We can do this using the ORDER BY clause.

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The rows are currently sorted by the ID field in ascending order.

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But if we wanted to sort by weight, we can say ORDER BY WAIT.

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This sorts by weight in ascending order, because that's the default sorting method when not explicitly specified.

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But we can change this to descending order by adding DESC now we can see who the heaviest player is

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when it comes to sorting data using order by this isn't something that's overly important for our use case

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because when we're selecting data to create views we're usually then going to connect

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that view to a bi tool and use

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that for building individual chart queries

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and these tools will let you apply sorts to the data

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now we're going to look at how to join data from different tables.

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To demonstrate this I'm going to use the player attributes table.

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We can see that in this table we have the player ID but not the player name.

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To be able to create a view containing the player name plus the overall rating

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we would need to join the data from the player attributes and player tables.

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I'm going to start by specifying player API ID comma date comma overall rating.

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Now I want to get the player name from the player table.

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How do I do that?

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Well because we're going to need fields from two different tables we need to specify which field comes from which table.

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We do this by writing the table name dot field name.

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So player attributes dot player API ID.

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Same for date and overall rating.

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Now we can specify, perhaps after the player ID, player dot player name.

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If I run this I'll get an error saying

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that there's no such column as player .playerName because we're saying that we're looking for it in the player attributes table.

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This is when we need to specify the joining of the tables using one of the four join types.

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I'm not going to go into them in this video

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but I'll probably do a separate video that explains them in more detail so don't forget to subscribe.

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So in this case we'll use an inner join

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and we'll say inner join player the player table on

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and now we specify what fields

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we want to use to create the join in this case we're going to use the player api id field

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which is contained in both tables

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and again we have to add the table name first dot field name equals player dot player API ID and run.

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Now we have the player name appended to the player attributes table data.

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Pretty clever right?

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But let me show you something even more clever.

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I showed you earlier how to use the as function to create aliases for fields.

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Well we can actually do this for tables as well which makes the query much cleaner.

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First I'm going to give the player attributes table an alias of a and the player table an alias of b.

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I just need to write these two letters after where I've specified each table.

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So a after player attributes here and b after player here.

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Once I do this I can now replace the table name for each field like this.

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You see, much cleaner.

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So, you may notice that there are multiple rows for each player on different dates.

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So how would we add up all of these different values for each player?

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Well, to do this, we're going to want to aggregate the data using the SUM aggregator.

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I'll add this to the overall rating field and run the query.

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Ah, that's not what we were expecting.

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But that's because we haven't specified in the query how we want to group the data together.

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This is when we need to use the GROUP BY clause.

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When we use the GROUP BY clause we need to add into it all of the fields that we need to group.

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So in this case essentially all fields apart from the overall rating.

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A .PlayerApiId, B .Playername, A .Date And run

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This is correct, but not exactly what we want.

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We want to combine all of the ratings for the different dates,

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so we actually want to remove the date field from the specified fields as well as the group by. and run.

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That's better.

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But let's clean it up by aliasing the sum overall rating as rating.

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Let's sort this descending.

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Order by rating descending.

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Strange that Ronaldo and Messi aren't in there at the top.

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Let's try and see why this is.

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Perhaps there are just more entries for some players when we sum them up.

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To find this out, we could add a count of b .playerName.

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And yes, in this case how about using an average?

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I'll replace sum with average, avg, and run.

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And there we go, that makes more sense.

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We're going to go one step further and filter this result.

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To do that we can use the HAVING clause.

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The HAVING clause is only applied to the result of the GROUP BY function, so isn't the same as the WHERE keyword that's applied before.

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And it's only applied to numeric values, so in this case I'm going to ask for only ratings above 85.

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I need to write it directly after the group by and before the order by.

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So having rating greater than 85 and run.

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Only 26 records.

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And there we have our final query.

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I'll beautify it and this is what it should look like.

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You can see that all of the SQL keywords are capitalised.

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And you can see that we've come a fairly long way in a very short space of time.

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We've only covered a very small part of SQL as a whole, so there's masses more to learn.

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But what I've shown you today is a really good foundation for giving you the confidence to dive deeper.

198

Did I not say that once we'd finished, you'd wonder what all the fuss was about?

199

If you got value out of this video, please do like, share

200

and subscribe for more videos like this one why not start with this playlist here as always thanks for watching

201

I've been Adam Feiner and until the next time stay BI curious

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Từ vựng và ghi chú luyện nói cho bài học này

Bài luyện nói này dựa trên video “Learn Basic SQL in 15 Minutes”. Những từ được nhắc lại nhiều nhất trong bài: player, table, field, row, select. Video này có 201 câu và 2466 từ để luyện shadowing. Phần lời nói dài 17:28. Người nói giữ tốc độ đều, khoảng 141 từ mỗi phút, vừa sức để nói đuổi theo. 84% số từ nằm trong 3.000 từ tiếng Anh thông dụng nhất; phần còn lại nên xem trước khi luyện.

Từ vựng quan trọng trong video

15 từ đáng học trong video, kèm phiên âm và nghĩa:

Từ	Phiên âm	Nghĩa
select động từ	/sɪˈlɛkt/	lựa
database danh từ	/ˈdeɪtəˌbeɪs/	cơ sở dữ liệu, CSDL
rat danh từ	/ɹæt/	chuột
operator danh từ	/ˈɑpəˌɹeɪtɚ/	toán tử
clause danh từ	/klɔːz/	vế câu, mệnh đề
attribute danh từ	/ˈæt.ɹɪˌbjut/	định ngữ
connect động từ	/kəˈnɛkt/	nối
null danh từ	/nʌl/	rỗng
subscribe động từ	/səbˈskɹaɪb/	đăng kí
chart danh từ	/t͡ʃɑɹt/	biểu đồ, đồ biểu
filter danh từ	/ˈfɪltɚ/	máy lọc
soccer danh từ	/ˈsɑk.ɚ/	bóng đá, bóng tròn
complicated tính từ	/ˈkɑm.plɪˌkeɪ.tɪd/	phức tạp
sensitive tính từ	/ˈsɛn.sɪt.ɪv/	nhạy cảm
curious tính từ	/ˈkjʊɹ.i.əs/	tò mò

Cụm động từ bạn sẽ nghe

Từ	Nghĩa
open up động từ	mở

Những câu đáng luyện lại

Các câu ngắn, trọn ý trong video mà bạn có thể dùng lại khi giao tiếp hằng ngày:

Let's jump onto my computer and get started.
In terms of the tool I'm using to query the database?
You see, it's not that complicated.
In this table we don't have any null values.
How do I do that?

Ngữ pháp trong video

Những cấu trúc người nói dùng nhiều nhất, kèm đúng cụm từ trong video:

Cấu trúc	Trong video
Câu điều kiện if + mệnh đề, will/would + động từ — điều kiện và kết quả	If I double click it we'll see · If I run this I'll get
Thì hiện tại hoàn thành have/has + quá khứ phân từ — việc đã xảy ra nhưng còn liên quan đến hiện tại	I've created · i've written · have been capitalized
Câu bị động be + quá khứ phân từ — nhấn vào việc xảy ra, không phải người làm	might be called · are often called · have been capitalized
Khả năng với “might / may” might/may + động từ — điều có thể xảy ra nhưng không chắc	might be · might want · may notice

Phát âm cần chú ý

Người nói dùng 70 dạng rút gọn, ví dụ we're, I'm, I'll. Hãy nói theo dạng ngắn đúng như bạn nghe.

Âm “sh” và “zh”: efficient /ɪˈfɪʃənt/, essentially /ɪˈsɛnʃəli/, dashboard /ˈdæʃˌboɹd/
Từ dài — đặt trọng âm cho đúng: operator /ˈɑpəˌɹeɪtɚ/, complicated /ˈkɑm.plɪˌkeɪ.tɪd/, essentially /ɪˈsɛnʃəli/, aggregator /ˈæɡɹɪˌɡeɪtɚ/, capitalize /ˈkæpətəˌlaɪz/

Những âm người Việt thường gặp khó:

Cụm phụ âm cuối — đọc đủ từng âm, đừng nuốt âm cuối: select /sɪˈlɛkt/, connect /kəˈnɛkt/, descend /dəˈsɛnd/, chart /t͡ʃɑɹt/, pound /ˈpaʊ̯nd/
Âm /tʃ/, /dʒ/ và /ʒ/ — tiếng Việt không có, đừng đọc thành “ch” hay “gi”: chart /t͡ʃɑɹt/, integer /ˈɪn.tɪ.d͡ʒə(ɹ)/
Âm /æ/ — mở miệng rộng hơn âm “e”: rat /ɹæt/, attribute /ˈæt.ɹɪˌbjut/, aggregate /ˈæɡ.ɹɪ.ɡət/, dragon /ˈdɹæɡən/, graph /ɡɹæf/

Cách luyện với video này

Nghe hết video một lần, chưa cần nói, và ghi lại những từ bạn chưa biết.
Nói đuổi từng câu ở tốc độ bình thường, lặp lại mỗi câu đến khi nhịp của bạn khớp với người nói.
Ghi âm giọng mình rồi so với bản gốc, chú ý các từ như select, database, rat.

Phương Pháp Shadowing Là Gì?

Shadowing là kỹ thuật học ngôn ngữ có cơ sở khoa học, ban đầu được phát triển cho chương trình đào tạo phiên dịch viên chuyên nghiệp và được phổ biến rộng rãi bởi nhà đa ngôn ngữ học Dr. Alexander Arguelles. Nguyên lý cốt lõi đơn giản nhưng cực kỳ hiệu quả: bạn nghe tiếng Anh của người bản xứ và lặp lại to ngay lập tức — như một "cái bóng" (shadow) đuổi theo người nói với độ trễ chỉ 1–2 giây. Khác với luyện ngữ pháp hay học từ vựng bị động, Shadowing buộc não bộ và cơ miệng phải đồng thời xử lý và tái tạo ngôn ngữ thực tế. Các nghiên cứu khoa học xác nhận phương pháp này cải thiện đáng kể phát âm, ngữ điệu, nhịp điệu, nối âm, kỹ năng nghe và độ lưu loát khi nói — đặc biệt hiệu quả cho người luyện IELTS Speaking và muốn giao tiếp tiếng Anh tự nhiên như người bản ngữ.

Phương pháp shadowing: đọc hướng dẫn từng bước đầy đủ →

Luyện nói tiếng Anh bằng Shadowing qua video: Learn Basic SQL in 15 Minutes | Business Intelligence For Beginners | SQL Tutorial For Beginners 1/3

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