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Short Class SQL Introductions for Data Analysis | MySkill

2:05:49EnglishBy MySkillTranscribed Jul 17, 2026
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10:11

Hello friends. Good night.

10:14

Does my voice sound clear?

10:18

Friends who are already in the chat column

10:20

have also greeted each other like that. Listen,

10:23

Sis. Thank you, Sis Salsa, it sounds

10:26

clear. Okay, thanks guys

10:28

for the confirmation. I would like to

10:30

remind you that

10:32

tonight we will be holding a class that

10:35

starts at 7:30 PM.

10:38

So while you guys are still waiting, it's totally

10:41

fine to

10:43

prepare your combat equipment, maybe.

10:46

It can be anything, Sis. Is it

10:48

okay to drink coffee or drink tea or

10:52

something else? Cai, boba.

10:54

What's more, my friends like this, which

10:56

usually brings back their focus. It's

10:59

definitely okay to prepare because we

11:01

still have 10 minutes left

11:04

before eating. Well, you can eat maca

11:06

with

11:08

ice, it's really delicious, right? Or

11:10

warm maca is also good for tonight.

11:13

Okay, I'm here but I want to get to know my

11:16

friends first. Friends,

11:18

friends, with friends

11:20

who are present here. What do you think your

11:22

friends' current status is

11:24

so it's easier to call them? Should I

11:26

call them Mas, Mbak, Teh, Kak, Bang,

11:30

or should I just call them friends? Of course you

11:32

can. Can we just spill the

11:35

beans,

11:36

Sis? Okay.

11:39

Ineng eh Inengah Retired.

11:43

What do you call her, Sis? I just

11:47

want to call him Kak. Sister Lulu PR

11:50

graduate, Sis is still a student. Fairus

11:54

mabaka. Wow, this is cool, Your Majesty.

11:58

Okay, Brother Yugi. Maybe some of you

12:01

know Kak Yugi or are acquainted

12:04

with Kak Yugi. Tonight there is Kak

12:06

Yugi Saiful U. What is his U? Ulala

12:09

maybe we don't know. Never

12:11

know, it's really okay. He said he was called His

12:13

Majesty, my friends.

12:16

Okay, here is Sis Fasya,

12:19

just friends, Sis. Okay, there's Sister Martina, a

12:23

student. There's Sister Salsya, Sister Ananda FR, who

12:26

graduated too. New Student Brother Hendarto Fresh Fresh

12:29

This Fresh is picked from the tree,

12:32

Brother. Here, Sis, there's a new student, Sis Asiraf or

12:36

Asraf, how about this, Sis, do you read it?

12:39

Press graduates Kak Ilham, Kak

12:41

Ferdiansyah. Just call me Honey, Sis. Oh,

12:44

positive thinking. The honey in question

12:45

is honey, friends. Then

12:49

there are also Mab students, Kak Ja, Kak

12:52

Lisander Fore. Okay, later Sis, you can

12:55

definitely prepare the fore if

12:57

you like fore coffee for me. If you like coffee, that's

12:59

fine, it's up to you,

13:04

whatever you want tonight. So that

13:06

our teaching this evening will be

13:08

better, it will go more into the brain.

13:10

Of course you can. There is Sis Lina, princess,

13:13

Sis. Ready, Princess.

13:17

Then who is there? Miss. Oh, Sis

13:19

Neng. Sis Nenga wants to be called Sis,

13:22

call her Ratu.

13:24

Oh my, Brother Onik is wearing a fresh graduate. Brother

13:28

Yugi wants his Majesty. Freshmen freshmen freshmen.

13:32

Ouch, Brother Lukman seems

13:33

a bit like Lukman Nur Hakim Cagur,

13:37

Sis. It's okay, Sis. There's still

13:39

next year. As long as the age is still

13:41

sufficient, capable, you can still try,

13:43

Sis. There's Sis, call me Sis. Okay.

13:47

Hello, Sis Anyya. There is Sis Adisah, a fresh

13:50

graduate. Sister Keizadiah called me madam, it was

13:55

so funny, I swear. Madam. Madam, what is

13:58

this? Madam Bamboo. High school, Sis. Oh, Brother Andi

14:02

is still in high school, friends. Call me

14:04

Princess, Sis. There's Brother Syah,

14:08

Brother. How many days will the service take?

14:10

Oh my, Sis Vivin, in the midst of us

14:12

joking around, Sis Vivin is still

14:13

asking for certificates. But it's okay, Sis

14:15

Vivin. I answered that the certificate would be issued no

14:17

later than 7 days after the form

14:20

was closed. So later, if

14:22

the form closes on the 15th, which is H+2

14:26

after this class, the test will

14:28

come out

14:30

7 days after the 15th, if I'm not

14:33

mistaken, that means the 22nd, right?

14:36

Just wait, my friend, there is Brother Farhan,

14:38

Brother. Okay,

14:41

we don't need to continue with the ones below that. Okay,

14:45

just friends. Okay. R graduate active student

14:48

proji. Active student proji.

14:50

What does that mean, Sis? Sorry, I'm a bit of a

14:54

student plus a housewife, Sis. Wow,

14:57

this is really cool, Sis Hikia Putri.

15:00

It means that you can manage your time very well.

15:03

There's Muhammad Ali, a final year student, right? He's

15:05

currently writing his thesis, right?

15:07

Scriptan.

15:09

Then there was Sister Alia who came from

15:12

Suriname. Where do you

15:14

think Suriname is, Sis? Just call me Bro, Sis.

15:17

Okay, there is Brother Ji Pratama. Let's call

15:20

Bro together, okay?

15:22

Call me Kim Tehyung, Sis. Just Kim

15:24

Tehyung

15:26

Jimin too. Princess K wkwk.

15:31

There is Sis Daska who wants to be called an angel,

15:34

bro King, I call her a princess at high school.

15:40

If registration is open, bismillah, bismillah,

15:42

Sis, I hope you will be accepted for your

15:45

dreams. My friends in

15:46

this class who currently really want to upgrade their skills

15:48

and this is what I want to be my

15:52

entry ticket to get a better career

15:54

.

15:55

Hopefully it will come true, friends.

15:58

Hopefully, it will be easier and

16:01

we can digest our material today and then become a

16:05

gateway to knowledge for a

16:07

broader, even better career. There's

16:10

Jini Sangian calling for jobs, Sis.

16:14

Jobjob Hipop's older brother's name is really cool

16:16

.

16:18

Hello, Sis. I'm Adinda. Hello, Sis Adinda

16:21

King. Okay, there's Brother Aprilian, we'll call him

16:23

King too.

16:26

Sis, will there be a link for the certificate later

16:28

, Sis? Okay, the certificate link is

16:30

usually for the certificate kim. I

16:33

will send it periodically every 10 minutes

16:36

or every 15 minutes in

16:38

this chat column. So, maybe later,

16:40

friends, where is the

16:42

certificate, where is the form, I will

16:44

send it here, maybe it won't arrive

16:46

in your email tomorrow, so it's

16:49

really okay to save it in

16:50

this chat column because it will be with friends.

16:53

Hi, Bro. Amen, Sis. Sis,

16:56

where do you get the minimum? I'll

16:58

share the details here later, Sis

17:00

. And after class, after the

17:02

explanation, I will explain later.

17:04

So. Will

17:07

there be a record of this later, Sis, on

17:08

YouTube? Yes, friends. Later,

17:10

I will also share the recording link

17:13

after class or during class

17:15

. So,

17:17

just monitor it later in case something is

17:19

missed. So, when I give you

17:21

important links, today's class link,

17:24

please save it. Maybe tomorrow,

17:27

"Sis, oh my, the email hasn't come in yet, it's

17:29

already 10 o'clock. It hasn't come in this morning."

17:30

Perhaps there are such cases. Later, my

17:33

friends will have a guide,

17:35

namely the class link that I gave you

17:36

tonight because it will be the same, right,

17:39

my friends.

17:41

Come on, Sis, it's starting to get impatient. Be

17:43

patient, Brother Muhammad Faktur. There are still

17:45

4 minutes left

17:48

according to the procedure, Sis. So

17:50

we'll start at 7:30 PM.

17:53

What will the task be like,

17:54

Sis? Is it okay, Sis Nabila?

17:58

I will explain the assignment later after the

18:02

presentation. So just stay tuned, sis.

18:05

Sis, this certificate doesn't appear

18:07

on the Myel website. That's right, Sis. Because for

18:09

example, if we share the certificate, we will usually

18:11

share it via the Telegram group.

18:13

Maybe some of you have

18:15

n't joined the Telegram group yet, where

18:18

we can get service information. You can

18:20

DM me on Telegram

18:22

later, I'll help you answer and

18:25

share the service link.

18:28

Hello, Sis. I am a PR graduate from the

18:30

Falak Science department. This is a total lie, Sis N.

18:34

I'm afraid it will be buried, Sis. I'll get the

18:37

links later, don't worry, friends. It

18:38

won't get buried because I will

18:40

send it every 10 minutes or every 15 minutes,

18:42

okay?

18:45

Sis, where is the FG FBJ background link

18:49

? I'll send it here too, Sis

18:50

. Just stay tuned, Sis. This is my

18:53

first time taking a class like this. I want to

18:54

ask, Sis. Is the Tibon uploaded

18:56

every class or just once, Sis?

18:58

Once a month, Friends.

19:00

So maybe some of you have

19:01

uploaded it in May and

19:04

now it's July and you want to take

19:06

all the classes in July. Is it okay to

19:08

upload it like that? But if,

19:10

for example, I joined in July and just

19:13

uploaded it yesterday and want to join the

19:15

classes in July, do I

19:16

need to upload it again, Sis? No need.

19:18

So, simply put, one wibon can be

19:22

used for 1 month. But if

19:23

the month has passed, is it okay, Sis?

19:25

Well, friends, you have to re-upload it

19:27

again. Oh, I see. Does

19:31

this mean training or

19:32

certification? Mm,

19:34

actually I can't answer this specifically,

19:37

friends. But maybe someone

19:39

can explain what training means.

19:40

If I were to get certification training,

19:44

maybe. Later, I'll try searching first to see

19:46

what the differences are. Because

19:48

Mimin himself doesn't

19:51

know the specifics.

19:54

Is there a deadline for submitting the assignments,

19:55

Sis? Yes, Sis. So H+ 2 after

19:58

this class is held, right? Kaktifnya ada masa

20:01

berwalainya tidak buat daftar pekerjaan saya.

20:04

No, Sis. As far as I know, we can use this certificate

20:06

as long as the knowledge is still

20:09

in our heads,

20:12

we can be responsible for the certificate, right? Sis,

20:15

the tibon is uploaded on Sosmate or on

20:17

Tiboniz. Oh yeah, don't get this wrong,

20:18

friends. Ee tibonnya is mandatory on

20:21

social media, not on WhatsApp.

20:23

Basically, on social media where

20:25

friends can be seen by the public,

20:27

for example Facebook, Instagram, Linkin,

20:29

Twitter and so on or TikTok is absolutely

20:32

fine. Except don't

20:36

post on Fibonac, friends, because it's the

20:38

same as not getting a

20:40

certificate. So we want Tibon

20:42

and Minitas to be posted on

20:44

social media. While waiting, we only have

20:47

1 minute left.

20:54

Sis, the tibon is indeed fresh grade,

20:56

Sis. My major is data analysis.

21:00

Ready, Sis. Sis, does the tibon have a caption

21:03

? There is, friends. Later when

21:04

you open the tibon, it will be there, but

21:07

if it's not there, you

21:08

can see it. Because, for

21:09

example, if there are captions there,

21:12

you can copy them or

21:14

adapt them as long as there is a

21:16

hashtag.

21:17

Okay, since it's already

21:19

19.30. You

21:22

can ask your questions at the end after

21:23

the presentation is finished, friends.

21:26

Especially for the technical class

21:28

tonight. So I'll just get started.

21:30

Asalamualaikum warahmatullahi

21:32

wabarakatuh. Hello. Om swastiastu. Namo

21:35

buddhaya and greetings of virtue. Good

21:37

evening, friends, whom I am proud of

21:40

tonight. Welcome back

21:42

to My Skill's Cass short class with

21:44

our theme tonight is

21:46

data science and analysis and the topic

21:49

is SQL Introduction for data

21:51

analysis. But before that, let me introduce myself, my name is

21:53

Kartika Simangkir. Friends can

21:56

call me Kartika or Tika. And

21:58

tonight I will be the moderator who

22:01

will guide our class

22:03

today. For those of you who are

22:05

joining MySkill for the first time and don't know

22:08

or are still unfamiliar with what MySkill is,

22:10

here I will explain a little about what

22:12

MySkill is. So

22:16

MySill is a skills development platform

22:18

with more than 2 million users

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throughout Indonesia and is part of the top

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Indonesian startup linkin from

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2022

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to 2024 and has been

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certified by the Education Alliance

22:33

Finel. What can we get from My Skill

22:36

, Sis? In My Skill, we

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can get more than 1,400

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e-learning materials and portfolio projects.

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There are also 12 intensive bootcams that

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focus on practice, which will certainly be

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very useful for training

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your skills. Then there is the

22:53

CV review by AI. I will explain one by one,

22:56

okay? Well, but before that

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, why do we have to choose My

23:02

Skill? Especially why we have to join the

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full stack intensive My Skill, huh?

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The reason is because more than 10,000

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MySkill alumni have been accepted by

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various top companies

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in Indonesia.

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For example, friends can see

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photos of our older brothers, sisters,

23:21

brothers, sisters, brothers, or friends

23:24

on my screen

23:26

tonight. Starting from Kamanda who is in

23:27

awap, Kak Fata, Kak Stefano, Kak Syifa,

23:31

Fahmi, and many more who are

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currently working in

23:34

top companies with titles

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or positions that are no

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less cool. The content is as a

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data manager for the Ministry of Social Affairs of the

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Republic of Indonesia as a social

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marketing specialist in the beauty industry,

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UIUX designer at PT Global Artisan Teknologi, UUX designer at PT

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Permata

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Indonesia,

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intern Fiber Optic Drafter at PT Telkom

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Access, and many more of course

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for his companies not

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only national companies but also

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are also several examples of

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companies where

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alumni work.

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For example, we can see below that there are

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Amarbang United Tractors, Member of

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Astra, Imuni, DBS, Ciputra, and of course

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many more.

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So, for those of you who are

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really interested in practicing your skills or

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deepening your knowledge in data

24:34

analysis, we have a full-stage intensive

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bootcamp coming up soon, which

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will be held from July 31st

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to September 18th,

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2026. What are the benefits? Well,

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the benefit is that friends will join a

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live class with our expert or narsun

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. Then there is a real project

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portfolio or original portfolio,

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which will of course be very useful. There are

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materials and class recordings, get

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e-certificates and career preparation classes,

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and access to over 1400

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elearning videos. And then from

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July 31st to September,

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you will get more than 17

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live online sessions and practice on

25:19

intro to intro to data analysis,

25:21

statistics in data analysis, data

25:24

formatting, data cleansing, SQL, Pown,

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joining myskill.id.botcam,

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15% discount with the promo code My

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via FIS or bank transfer. So,

25:45

where are the experts from, Sis?

25:47

The experts are of course from

25:49

top companies, friends.

25:51

For example, there are Pamar Persada

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Nusantara

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or Finansial Fim Niaga and

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many more. Well, for friends, it's not

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just a bootcamp, but we also

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have a discount for e-learning by only

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paying around IDR 40,000. Friends

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can access the material for 6 months and

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get material on digital

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Don't forget to use the promo code

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Payment can be made via

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26:33

Well, this is especially for those of you who are

26:34

currently still wondering, "Sis, I

26:36

've sent so many CVs, so many

26:38

applications to companies,

26:40

but why haven't I received any calls, Sis?" Oh, I see

26:41

. Especially for fresh graduates

26:43

or perhaps fellow

26:45

students who want to find internships or

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friends from high school who also

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want to expand their connections and

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gain experience. This is the

26:54

solution. My Skill has a solution,

26:56

friends. So we can upgrade

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our CV to be even better

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with the CV review program by AI.

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CVs in 2 months. Then, there will be

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CV revision results, analysis of

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the suitability of career choices and skills,

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your CV, and application letters will also be made

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based on your CV, and of course, there will

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be interview practice, HR, and superiors.

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And finally there are complete documents

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Reqc voucher code.

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our promotions and profit strategies via the

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link listed on my screen.

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Okay, next one. So, for those of you

28:34

who are still wondering what the

28:36

benefits are, what are the benefits

28:38

we get if we join My

28:40

Skill's class? My Skill short class. The

28:42

first thing that you are most

28:43

looking for is an e-certificate.

28:45

Then there are practical, practice and Q&A

28:49

with experts

28:50

based on case studies, practice in

28:53

creating a mini portfolio and also a

28:55

question and answer session. And in addition, as

28:58

I mentioned before, there are

28:59

discounts on bootcamp training, recordings of

29:01

class materials, and lifetime community

29:04

membership.

29:06

Well, for those of you who are still confused

29:08

, where can

29:12

we include our CV,

29:14

where can we

29:16

include our certificates? The

29:18

first one can be in your CV or you can link it to

29:21

your profile or post. For example, you can

29:23

see it on my screen on

29:25

the left. Later, you can just

29:26

enter the CP by entering the

29:29

bootcampik

29:30

skill period and certificate link as well as the portfolio link

29:34

or in the post or profile of the

29:37

LinkedIn post in the lense and

29:39

certification column. Don't forget to also

29:41

write down skills or bags

29:43

related to the certificates that your

29:44

friends have.

29:47

Well, this is what we've been waiting for.

29:48

Tonight we will be taught directly

29:50

by our expert, namely Ms. Hana

29:52

Rosuliana, who currently works as a

29:55

business data analyst at a Financial

29:57

Technology Company. Well, coincidentally

29:59

this evening Sis Hana is here

30:01

with us and

30:02

we are really welcome to say hello. Hello, Sis Hana.

30:07

Hello

30:08

Pratika and all my friends.

30:11

Hello, Sis. How are you

30:13

tonight, Sis?

30:14

Alhamdulillah, fine. Thank God,

30:16

Sis. Okay, without further ado,

30:18

Sis, because my friends also

30:20

really want to listen to the presentation

30:21

from me. I'll move on to the

30:23

next slide before you take

30:25

over, okay?

30:28

Okay, let's move on, friends, without

30:30

further ado, shall we? So, these are the

30:32

class rules that are absolutely mandatory

30:34

for Patuji friends

30:35

tonight. First, friends are asked

30:37

to mute the mic and disable

30:40

annotation. Then, the class recording

30:42

will be shared after class. So for

30:44

those wondering if the recording will be

30:46

shared? Yes, it will be shared,

30:48

friends, and the material will not be

30:50

shared with participants. So please

30:52

note down the important points or you

30:54

can study again later through the

30:56

recordings that we will share

30:57

later. Fourth, there is no

31:00

absence form. There is only a certificate form.

31:02

Where did you get it, Sis? Don't worry,

31:04

I will share it via the chat column and there will

31:08

be a QNA session or question and answer session

31:10

using Slido.

31:13

I will also send the link via the chat column. And

31:16

finally, friends are asked to be

31:17

100% present and focused on attending the class.

31:21

So, these are some of the steps to

31:23

get the e-certificate. The

31:25

first is to upload the wibon to

31:27

social media. Social media,

31:29

guys. It's not WhatsApp, nor is it a

31:32

private account, our social media

31:34

is publicly accessible, so when

31:37

friends include the link,

31:39

it's easier for Mykel's team to check

31:41

whether it complies with the provisions or not.

31:43

For the link, you can go to

31:45

tibonize.com/shortclasskelton.

31:48

Later, friends can

31:49

use the captions listed when

31:51

accessing the tubon or if you don't find them,

31:54

you can also copy and paste them or adjust them

31:56

to suit your needs. But

31:58

don't forget to include

32:00

hashtags like the ones below

32:02

. Next. Well, then later we will

32:05

provide a link to the minimum requirements independently

32:07

that you have to work on. When

32:09

working, don't forget to write

32:11

the name in the bullet point or in the

32:13

right corner like this, friends.

32:17

Then, after your friends have completed

32:18

their mini projects, they share them or post them

32:21

on social media. For social media,

32:23

you can use Linkin, TikTok, Facebook,

32:26

whatever you think is

32:28

public and

32:30

social media, you can share it

32:33

there and don't forget to include the

32:35

hashtag learn atmill. So, what must be

32:37

uploaded to our social media

32:40

are the results of minimal work,

32:42

such as the example on the side.

32:44

This is it. Meanwhile, the post that I will

32:47

send later does not need to be posted,

32:49

just do it in the form.

32:52

So, after you have fulfilled all

32:54

the rules above, you are

32:56

free to open the certificate claim link

32:58

. Well, there you will be

33:00

asked to upload the

33:03

twibon post link and its details. Don't

33:05

forget to include it, friends.

33:07

Share and copy links from

33:10

friends' posts. Here is the Twibon, then

33:12

here is the mini bag and also the

33:14

minitas file will also be placed on the ee form

33:17

later, okay? This will be closed H+2

33:20

after class. So, this is now on the

33:23

13th and will close on the 15th at

33:25

2359

33:27

and then for the certificate you will have

33:29

to wait a maximum of H+ 7 after the

33:32

form closes. So, H+ 7 after the 15th

33:35

.

33:37

Finally, we would like to remind

33:38

you to always be wary

33:40

of suspicious messages claiming to be

33:41

from MySill, such as

33:43

those inviting you to make sales, affiliate offers,

33:45

and so on, outside the

33:48

official MySill platform. If you receive

33:50

these messages, please

33:52

report them to us via WhatsApp

33:54

or the official my Skill Telegram

33:56

below. So, there are three official or

33:59

official contacts from My Skill, namely

34:02

WhatsApp customer service, official

34:04

Telegram customer service, and official

34:06

Telegram Mycale. Don't forget to capture

34:09

or screenshot it in case your friends

34:11

need it. Losses

34:13

arising from messages from

34:15

external parties that are not affiliated with

34:17

Meskill are not the responsibility

34:18

of Well, next, friends, you are welcome to

34:21

take out your cellphones

34:23

tonight, we share our productive activities

34:25

on our respective social media and don't

34:27

forget to include

34:29

#learn@mskill and don't forget to tag mein

34:32

my scale on Instagram, TikTok,

34:34

Linkin or Twitter. next. Well,

34:37

since we've come to the end,

34:40

I'll just send it or

34:43

hand it over to Sister Hana for

34:46

further material. Please, Sis Hana.

34:49

Hello, thank you, Sis Tika.

34:52

Okay, I'll try to share the screen first

34:54

.

35:01

Yes Is my screen visible yet,

35:03

Sis?

35:07

Okay, Sis.

35:08

Okay, ready. If so, I'll get started,

35:11

Sis. Sorry, let's slide show for a moment.

35:26

Okay.

35:36

Okay. Can you see it, Sis? My share

35:39

screen

35:40

looks very clear, Sis.

35:43

Okay, ready. Hello, good evening everyone.

35:46

Good night friends.

35:48

Hey, welcome to the Introduction

35:51

to SQL class. First of all, let me introduce myself here,

35:54

my name is Hana.

35:57

Hey, currently I'm a data analyst, you know

35:59

. Well, tonight we will learn

36:03

about SQL. Ee, I hope that from

36:06

tonight's class, my friends will learn a lot of knowledge that my

36:08

friends can learn and that will be

36:10

useful for my friends in the

36:12

future.

36:15

Hey, for example, if I saw earlier, yeah,

36:18

yeah, when I was still interacting with

36:21

Kartika, she said that there were still many

36:23

students, some were still in high school,

36:25

yeah. So I assume there are still a

36:28

lot of beginners here and of course, don't worry

36:31

because this class is

36:33

really suitable for you.

36:35

So, there's no need to worry for

36:37

those of you who have never used SQL, who

36:39

have never known what SQL is

36:40

like before. ee we will study

36:43

tonight like that. So the goal

36:44

of the class is for friends to understand, ee,

36:47

what the basic concepts are, starting from what a

36:49

database is, what SQL is, and then ee,

36:52

why SQL is important. Until

36:55

finally, friends will also learn

36:56

how to write queries like that. Later

37:01

in the last session, we will also have a

37:05

practical session, okay? We will try to

37:07

write a query live together so that our

37:09

friends will have an

37:11

idea and can

37:13

try it out straight away.

37:16

Then ee ee before we start, also from

37:19

me, ee, if it is possible,

37:21

friends can join the class

37:23

using a laptop or PC because later

37:26

we will have a practical ee and it

37:28

will be easier if

37:29

friends use a laptop directly.

37:31

So we can try it together right away

37:34

. Maybe that's all before we

37:36

start, I'll move on to the next slide.

37:41

Okay, let's get straight to it. So, before we get

37:44

into SQL, we have to understand first that there is something

37:46

called a database.

37:48

What is a database? So, in simple terms, a

37:51

database is a collection of data

37:54

or information that is stored in a

37:57

structured manner.

37:59

For example, in our

38:01

daily lives, we often find data

38:03

in the form of tables. For

38:05

example, student data or there is also

38:09

transaction data, customer data or

38:12

maybe also data from our short class participants

38:15

tonight. All of that, ee,

38:17

is data and we definitely

38:20

find it in our

38:21

daily lives. There must be data for everything

38:23

and it is recorded in the data.

38:26

Well, in our spreadsheet, the

38:29

data is in the form of a table, we

38:31

have rows and columns. In the database it is

38:35

also the same, usually it is arranged in the

38:37

form of a table, where each row

38:41

represents one data or one

38:43

record.

38:44

Meanwhile, each column will

38:47

represent certain information.

38:50

Well, for example, if

38:52

there is a student table, we

38:56

can have columns such as student ID, the

38:59

student's name, and

39:02

also information such as

39:04

blood type, IP, and even date of

39:06

birth. It's all stored in

39:08

columns and ee later, for example, the

39:11

student's name will be in one row, that's the

39:13

data. Well, if

39:17

the spreadsheet is usually used for

39:19

relatively small data, or

39:22

also manually, if the database is

39:24

used for

39:27

larger data, that is already

39:30

larger. Apart from that, we also frequently access the data

39:31

and need to use it by

39:34

many people at the same time, you know, it

39:37

's used all at once. Therefore

39:39

we will need a system. So

39:42

we need a database, we

39:44

can no longer use simple data

39:48

in spreadsheets

39:50

. Then next.

39:53

So, why do we need a

39:55

database? What are the actual benefits

39:57

of using a database?

40:00

Well, of course there are many benefits. The

40:04

first one is that the database will

40:07

process it. By using the database

40:09

we can process data to be

40:11

[clears throat] easier and also

40:12

faster.

40:14

Why is that? Because in this database, the

40:17

data form will be

40:20

structured like that. So we

40:22

will process it more easily and

40:24

also ee if for example the data is small

40:27

maybe the spreadsheet is still enough.

40:28

But if the data is already in the thousands, millions

40:31

, or even tens of millions of rows,

40:33

we need a more robust or

40:35

structured system.

40:37

Secondly, the database is ee with the

40:40

database data integrity is

40:42

better. This database will help us

40:45

maintain data integrity. So, the

40:48

stored data will be neater and more

40:51

valid, and it will not be easily damaged

40:52

, because it is stored properly. So,

40:55

even if we process ee data

40:58

many times, we process it a lot

41:00

or it is accessed by many people, the

41:01

data will still be stored

41:03

properly and ee is safe. So it won't be

41:06

damaged or anything. Then also the

41:09

third one, with the database data that

41:12

we have, what we store

41:14

will be more consistent. For example,

41:17

customer data, for example, we have

41:20

several data, such as

41:22

customer data or transaction data and also

41:24

product data, we can then

41:27

connect them to each other and

41:30

the results will definitely be consistent

41:32

because there is already a table that maintains

41:35

each of these data. And

41:37

what I said earlier is that the data

41:39

will not change even if we process

41:41

or access the data many times and

41:43

simultaneously.

41:45

Ee next, the last one is

41:48

the benefit of the database, also with the

41:50

database we have

41:52

more efficient storage.

41:54

Hey, so we don't need to

41:56

store a lot of data, repeatedly in

41:58

many places. For example, I want to wear it,

42:00

and for example, there is Big Sister Tika who wants to

42:02

wear it too. That's enough for us to save it just

42:05

once. but we

42:07

can both access it. Those are the

42:10

benefits of databases. So, it's

42:12

based on those benefits, right? We

42:14

have seen that

42:16

this database is very powerful and very important

42:19

because almost all modern applications

42:22

now also use a

42:24

database behind it. Okay.

42:30

So, now that we understand databases,

42:33

let's move on to the relationship

42:35

between databases and SQL, shall we?

42:39

So, the database is the place where the

42:42

data is stored. While SQL

42:45

is the language we use to

42:49

interact with the data. So,

42:52

I'll repeat maybe one more time, okay? So,

42:54

the database is where the data is

42:57

stored. Meanwhile, SQL is a

43:01

language that we use to

43:03

interact with ee data.

43:06

Maybe you're still

43:07

a bit confused from the definition. I'll try to continue first.

43:10

So, if we want to

43:12

retrieve data, for example, or

43:15

change it, add it, delete it, or

43:18

interact with the data, we

43:20

use SQL. So SQL is a

43:24

communication tool.

43:29

Firstly, there is another term called DBMS

43:32

. So what is DBMS? In BMS,

43:37

it is a system that manages the database

43:41

. So the database doesn't

43:43

run alone. There's someone

43:45

who manages the database, right? So no no

43:48

no just standing alone. There is a

43:50

system that manages what we usually

43:52

call a database management system or

43:54

we shorten it to BMS.

43:59

Okay, in this BMS ee with BMS ee will

44:04

allow its users

44:06

to create, access,

44:09

or manipulate databases. So, for

44:12

example, the database is the

44:15

storage place.

44:17

Okay, if the database is the

44:20

storage place, then in BMS

44:23

we can think of it as the guard, the

44:25

guard or manager of

44:28

the storage place. So, for example, we

44:31

are users, we as

44:34

users use an application,

44:36

right?

44:38

Well, this application doesn't directly

44:41

manage data like that, manage data

44:44

or files manually, but

44:47

this application will connect and

44:49

communicate with eh sorry and

44:51

communicate with the BMS.

44:55

Well, then in the BMS this is what will

44:57

manage the ee process to the database.

45:00

So, the way I try to repeat it once

45:02

again from the user or we the users

45:06

will use an application and the

45:08

application will definitely ee ee by

45:11

using the application the application

45:13

will definitely access the data ee so that it

45:16

can run where the data is

45:18

stored in the DBMS.

45:21

So, in this BMS, we will

45:23

manage the process from the database.

45:29

So that's more or less how it

45:32

works. For example,

45:34

if we open an

45:36

e-commerce application, and we

45:39

want to see our order history and see

45:42

what we have ordered, the

45:44

application will request the database

45:47

via DBMS to see the

45:51

transaction data that we want to see.

45:53

Only after the application

45:56

gets the data, it displays it in

45:58

the application, displays it back on

46:00

our screen. So in this BMS, ee plays a role

46:03

as a manager and liaison between

46:06

users, applications, and databases.

46:12

There are many BMS that are used like that, right?

46:15

Some popular examples

46:19

here are MySQL, Postgra SQL,

46:23

SQL Server, Oracle, and

46:28

other databases. Ee

46:30

[snort] actually in

46:31

principle each BMS

46:34

has a similar function, namely to

46:37

manage the database. But usually

46:39

the difference is only in the syntax or in the

46:41

detailed features. Well,

46:44

but specifically in this class we

46:47

will try the most common approach

46:49

or one that is starting to be understood, okay?

46:52

Hey, later for practice we will

46:53

use SQL which is also free and we can

46:56

access.

46:59

Okay,

47:02

now we come to the

47:03

main question, what is SQL? So SQL

47:07

stands for structured query

47:10

language. So this is a

47:14

programming language that is used to

47:16

access, change, and manipulate

47:19

relational-based data. So SQL is a

47:22

language, the way we

47:25

communicate is SQL.

47:30

Well, eh, SQL is known to follow the

47:34

American National Standard

47:36

Institute or ANSI Standard which is used in

47:39

relational database management.

47:41

like that. There are three main types of operations

47:44

in SQL. The first is creating an

47:47

object in the database, which we usually

47:49

call a data definition

47:52

language.

47:53

Furthermore, there is also taking data

47:56

from the database or also called data

47:58

query language and also changing data in the

48:02

database, namely data manipulation

48:04

language.

48:07

Well, for beginners,

48:09

we usually use it most often

48:11

in the data query language, right? So

48:15

we usually take

48:17

data using silect, I'll

48:20

start bridging a little bit with

48:22

the queries that we'll use

48:24

to write later.

48:27

Okay,

48:29

next. So in SQL, there are several comments

48:33

that we usually use

48:37

to create this database, there are three

48:40

comments. The first one is create. Create

48:43

this if for example we want to create an

48:45

object like that. So, the objects in the

48:49

database are databases and then there are

48:52

tables too. So, for example, if we want to

48:54

create something, then we use the

48:58

create command. For example, if

49:01

we want to delete it, we can also

49:04

use the drop command and if

49:07

we want to change the object, there is the

49:11

alter command.

49:15

Then ee next type of comment

49:18

get. So to take it

49:20

, to take the data,

49:23

we usually use select. This is what

49:26

will be used most often by data

49:29

analysts or business analysts.

49:32

Then the last thing for the

49:34

SQL comment is changing or manipulating.

49:40

There are three comments here, namely insert

49:44

to enter data, continue update

49:47

to change data, and delete to

49:50

delete data.

49:55

OK,

49:58

next we'll get into the

50:01

SQL functions.

50:02

So in general, SQL has two

50:05

functions, namely to change or

50:08

access the database or perform the

50:11

required queries.

50:13

And also the second ee functions as a

50:15

link between applications and

50:18

various databases. For the

50:20

first one, ee changes or accesses

50:23

it, for example, if we want to see

50:26

how many transactions there are this month. Then,

50:30

for example, if we want to calculate

50:32

sales, how many are there per city

50:36

? For example, how many

50:37

sales are there in Jakarta, how many are there in Bandung,

50:41

for example, if we want to find out who the customers are who

50:44

make the most transactions

50:46

. it is a SQL function that changes

50:50

or accesses. Then, if the ee

50:56

functions as a link between the

50:58

application and the database, for example, it's

51:00

like when we log in to the application.

51:03

Well, later the application will check the

51:05

user data in the database. So when

51:08

we make a transaction, the transaction data

51:10

will be saved by the ee application in the

51:13

database. So, SQL is

51:17

not only important for

51:19

data analysts, it is not only important

51:21

for retrieving data, but it is also

51:23

important for developers, data engineers

51:26

or many other professions who

51:28

use ee data to work.

51:34

Next, we will go into the

51:36

SQL data type. So, here SQL

51:40

has many data types, right? So it's

51:42

not just about the data, it's just like

51:44

okay, just name, phone number, and

51:47

also the date, that's all. But we

51:50

have to know what the data types are

51:52

like and what their definitions are

51:54

. So for that data type,

51:58

there are three big data types, right?

52:02

So the first one is character,

52:05

numeric, and temporal. And this is important

52:09

for us to determine what type of data

52:12

and what type of values ​​we can

52:15

store in that column.

52:18

Here we will try to

52:20

use postg.

52:23

Well, for the most common data types

52:26

for characters, there are two here,

52:29

namely varchar and text. If

52:32

varchar is for strings with

52:34

varying sizes.

52:37

Then for text there is a string

52:40

with unlimited size. So, it's like that,

52:43

it's not like that, it's

52:47

not unlimited. We can write

52:50

ee, the data content can be as long as we want

52:53

because it is unlimited. Then secondly

52:56

there is numeric. These numbers include integers,

53:00

floats, and bulls. This integer is a

53:03

whole number. So it's like 1 2 3.

53:07

Meanwhile, flot is a

53:09

decimal number that has commas. So

53:12

like 0.5, 0.7, 1.2, basically it's not

53:16

round or round like that. He's in a coma.

53:19

Then the third numeric is

53:21

bull or bulean. This is like right

53:24

or wrong, true or false. Or

53:27

maybe there are usually those who replace

53:31

true with 1 and false with 0.

53:35

And finally, there is the concept of data ee eh

53:39

there is a temporal data type, namely date ee

53:42

which is a date. So the format is

53:45

usually year, month, and day. Ee

53:49

contains three

53:52

ee there are year, day and month. And

53:56

then there is the

53:58

time stamp, which is the time stamp.

54:01

So the date is not just a date,

54:03

not just the year, month, and day, but

54:06

also the time. The time is also

54:12

recorded, starting from the hour, minute to second.

54:19

Okay, here we will try to see an

54:22

example, okay? Hey, so from

54:24

these types of data, for example,

54:26

how can you, friends, be able to

54:28

draw a better picture?

54:31

So for example here first there is the

54:33

student ID. This student ID contains ee 1

54:39

2 3 4 like that. So this ee is included in the

54:43

integer data type ee because it is a

54:45

whole number. Then here there is also this

54:49

because this is student data, so here

54:51

there is the ID, name, blood type, IP

54:54

and date of birth. Well, then for

54:57

this name, the type is farcar

54:59

.

55:00

Because

55:03

this eechure can be used for texts that are ee

55:06

varied but usually still limited.

55:09

Usually, people don't use names that are

55:11

too long, like descriptions

55:14

or, for example, book summaries.

55:19

What does that mean when it's long

55:22

? Ee to text. Meanwhile, if the

55:25

ee is still limited, it is called varer.

55:28

So the name is included in the variable data type

55:31

. Then there is blood type.

55:34

This blood type is still included in the

55:37

farchar section, but it has another

55:39

specification that we call enum,

55:41

okay? So it's text but it seems like the

55:45

choices are already limited.

55:48

So we're just like there

55:51

are only four blood types, A,

55:53

B, AB or O. Well, if

55:56

the choices are limited like that,

55:58

we can include the enom data type.

56:01

Then there is IP. This IP is a float

56:05

because it is in the form of a comma. Like Arif's

56:08

IP is 3.98,

56:11

Hadit's IP is 3.42. So, this

56:14

includes the ee data type flot.

56:17

Then the last one here is the

56:18

date of birth. If it's a birth date,

56:21

people usually only get to the date

56:23

, it's very rare for it to arrive. Ee,

56:25

we never even see if, for example,

56:27

we are asked for personal data, the

56:29

end date, right down to the last minute,

56:31

no. So because ee only contains

56:34

the date, month, and year, it is

56:37

included in that data type

56:40

. So, friends, it's

56:44

important to understand data types,

56:46

so that later we know

56:49

when we write queries, we won't make

56:51

mistakes because we can't

56:54

retrieve data if it doesn't

56:57

match the data type stored

56:59

in the database.

57:04

[clears throat]

57:05

Also, from this example, we can

57:07

see that for example, every data

57:10

must have its own data type, the

57:12

appropriate data type. We can't

57:14

just call them

57:16

integer data, for example, or call them all

57:19

vchures, but they must

57:21

contain data that matches their

57:23

respective types.

57:28

Okay.

57:32

Well, let's continue. Hey, now what is

57:35

the role of SQL in the industrial world?

57:41

So, SQL is widely used by

57:44

people in various professions, right? Ee

57:48

as here SQL is also used by

57:52

developers to write

57:54

data integration as well as by data analysts to

57:57

organize and run analytical queries

57:59

. So, there are indeed a lot of people who

58:03

use ee data in various professions.

58:06

Any profession that uses data

58:08

will definitely require SQL, right?

58:12

As previously, developers use it to

58:14

write data integration. Meanwhile,

58:16

data analysts organize and

58:19

run analytical queries. There are also

58:22

friends who must have known

58:25

a lot about data, knowing that

58:28

usually data is a profession, there are

58:31

also data engineers or

58:33

business analysts. Well, data engineers

58:35

use SQL to build

58:38

pipelines and process

58:41

large amounts of data. Meanwhile,

58:43

business analysis is more or less

58:45

similar to data analysis. But

58:47

usually, they are specific to

58:50

answering business questions, right? So

58:53

SQL is a

58:55

very important EE skill for

58:58

anyone who wants to work in the field of

59:00

data, technology, business, or EE in the

59:04

operational field.

59:11

Okay, now we're on the

59:13

last slide, there are examples of

59:16

professions that require SQL, right?

59:18

As I've already tried to

59:20

give an example, there are also data scientists, ee, who

59:24

usually

59:27

use SQL to retrieve data

59:30

before carrying out further analysis,

59:32

or ee, they usually

59:35

create machine learning models, right?

59:37

Well, they need to retrieve the data

59:39

using SQL. Then also

59:42

for data engineers, they build,

59:45

uh, use SQL to build

59:48

or manage the data flow.

59:51

So, those who manage it are usually

59:53

data engineers, then there are also data

59:56

analysts or business analysts who

59:59

will make reports here, right?

1:00:02

making reports, doing analysis, ee

1:00:06

looking for insights from existing data

1:00:08

, like that. Then there are also database

1:00:11

administrators who are

1:00:14

responsible for managing

1:00:17

the performance and security of the database.

1:00:20

Then there are also quality assurance

1:00:22

testers. Eh, here they

1:00:25

usually use SQL to check

1:00:28

[clears throat] whether the data that appears is

1:00:30

correct. ee the stored data is

1:00:34

clean, it is good, there is no more

1:00:36

anomalous data that does not match the

1:00:40

ee data stored in the database.

1:00:43

So, SQL is a skill that is not

1:00:47

only used by one profession

1:00:51

, but there are many professions

1:00:54

that use SQL.

1:00:57

So, SQL is indeed a

1:01:01

cross-functional skill that is used in many

1:01:03

fields.

1:01:06

Okay,

1:01:12

now we'll move on to

1:01:16

that material for now. Now we will move on

1:01:19

to the demonstration, to the practical part. So

1:01:23

in this session, our goal is

1:01:26

not to immediately become an

1:01:29

SQL expert, but our goal is

1:01:32

more for us to learn and understand the

1:01:34

basic patterns of queries first.

1:01:38

So later I will try to start from the

1:01:41

simplest ee query. Then

1:01:43

later we will try, for example, adding a

1:01:45

filter or adding a sequence or

1:01:48

calculation like that.

1:01:50

And if, for example, when we

1:01:52

practice like that, I also hope that

1:01:54

while trying, if for example there

1:01:57

are still errors or mistakes, that's totally okay

1:01:59

. Instead, we have to

1:02:01

try and learn until

1:02:05

we can finally write queries without

1:02:07

errors. But errors are also

1:02:10

not a problem at all. Even

1:02:12

experts often still make mistakes

1:02:14

when writing queries. Whether it's

1:02:16

an error, it's usually just a typo.

1:02:19

there is something written wrong. So, it is

1:02:21

still very normal for errors to occur.

1:02:24

J ee maybe that's the bridging before

1:02:28

we start the demonstration.

1:02:31

Before I continue here, friends, are you

1:02:34

ready for us to continue

1:02:36

the presentation, ee, to the demonstration?

1:02:44

Okay.

1:02:49

Okay. So for the demonstration here

1:02:51

we need to prepare two of them, okay? The

1:02:54

first is the data set. The data set and

1:02:57

the second one is the ee tools.

1:03:00

Friends, you have got the link for

1:03:05

the ee database and the SQL link, right?

1:03:11

Okay, safe. So here we try it

1:03:14

together, okay?

1:03:31

Okay, first I will open the

1:03:35

data set.

1:03:37

This is already visible, right? There's already

1:03:40

the syntax here. Then I'm also

1:03:45

going to open SQL Lite

1:03:57

SQL. Let

1:04:01

me try to refresh first.

1:04:05

OK, this is the default view for SQL.

1:04:13

So, ee, this UI here already has a

1:04:19

default one, right? So,

1:04:21

we can just delete the star select from this demo

1:04:24

.

1:04:26

Then we can open this

1:04:29

free database, friends, you can download it, you can

1:04:31

download it first or you can directly

1:04:34

access it from here.

1:04:38

If I were here I would try to

1:04:40

give an example of ee for direct access, okay?

1:04:43

Because it's already open here,

1:04:45

we can just copy it straight away. So we

1:04:49

select from the top, we select and then we

1:04:53

copy. Ctrl C. Then we open it again to

1:04:57

ee SQL online.

1:05:00

For example, if you look on the left

1:05:02

, there is a table here and the table

1:05:04

here is just a demo table. There is only

1:05:07

one table which is demo. Now we

1:05:10

will try to insert or create a

1:05:12

table in SQLite using the database

1:05:16

that we copied earlier. So let's

1:05:19

copy and paste it here.

1:05:24

Okay, this has a query

1:05:28

from the beginning, from top to bottom. Then

1:05:31

we can directly press run here

1:05:33

or if you want to use a

1:05:35

shortcut, we can

1:05:39

press control enter, which is the same as

1:05:42

pressing run here. I

1:05:45

'll try using run first,

1:05:47

use the run button here.

1:05:50

Well, after we run it, the

1:05:55

ee table appears here, the transactions. If

1:05:58

only there was a demo, right? Well, here

1:06:01

the transaction has appeared.

1:06:08

Okay, it's

1:06:11

safe, guys. It's

1:06:13

all been successful to access the database.

1:06:21

Okay.

1:06:23

Okay. If that's the case, it means that

1:06:26

all my friends can do it and the

1:06:29

transaction table has appeared here.

1:06:34

If so, we can just delete the query

1:06:38

. So we don't have to save this query

1:06:40

. And we can see that

1:06:42

for example the data in the table

1:06:45

already exists and is stored in SQL over here

1:06:48

.

1:06:53

Okay, then we'll continue.

1:06:56

We will try some queries for

1:06:59

our ee practice together here. The

1:07:01

first thing we will definitely try is the

1:07:04

most basic one. The most basic one is

1:07:07

definitely a query called

1:07:09

select.

1:07:11

We have already created a table

1:07:14

called transactions. Now we want to

1:07:16

see what the contents of the transaction table are like

1:07:19

. So we're going to look at all

1:07:22

the data, the entire data. We can

1:07:25

use the ee syntax, namely select star.

1:07:29

So this star means we will ee

1:07:32

take all the data. Here we

1:07:35

will try to run.

1:07:39

Okay,

1:07:40

sorry for the

1:07:43

short delay.

1:07:46

Oh, sorry star. So, for example, if

1:07:50

our query has an error, it

1:07:53

will appear like this,

1:07:56

SQL error, and there will be a definition of the

1:07:59

error. So here it is

1:08:02

informed if for example not table

1:08:04

specified. So there is no table that

1:08:06

we can choose. Sorry. For

1:08:10

example, if we select it, we should

1:08:13

define it clearly, right?

1:08:17

We have to mention everything. So

1:08:22

what is the name of the select star from table? We have a transaction table.

1:08:25

So we will take all the data

1:08:28

from the transaction table.

1:08:31

then we run this.

1:08:34

Well, here the

1:08:38

transaction data that we just entered

1:08:41

into the transaction table immediately appears.

1:08:43

Can you see

1:08:46

how many columns there are here? 5

1:08:50

11 columns. There's a transaction ID, there's a

1:08:53

date, there's the owner's name, right?

1:08:57

We can just move this around. For

1:08:59

example, if we want to see the definition, or want to

1:09:02

see the full form, or

1:09:05

what the table or column is called,

1:09:07

we can just open it. Or, for

1:09:09

example, if there is something called

1:09:11

cover-up, for example like this and we

1:09:15

want to see who Dewi is, this is

1:09:17

flexible, we can move it so it can

1:09:20

be seen, but it will still

1:09:24

adjust to the tools, depending on the tools

1:09:26

we use. Some can

1:09:27

shift, some can't.

1:09:31

There is the region of origin, then there is also the

1:09:33

quantity,

1:09:35

there is the number of items, then there is the

1:09:39

unit price, there is the total payment, there are

1:09:44

payment methods and also there is the status ee

1:09:48

the payment status should be like that.

1:09:55

Okay, that's easy, guys. Of course it's

1:09:57

still easy.

1:09:59

We're still just being silly.

1:10:01

If so, then next time

1:10:04

, maybe I'll explain a little.

1:10:06

If you select this star, that's the

1:10:09

syntax for retrieving all data.

1:10:11

This is usually

1:10:13

useful, but it is not recommended

1:10:16

if our data is already

1:10:19

too large. usually, if

1:10:21

the data is already hundreds of thousands, millions,

1:10:24

hundreds of millions, especially if

1:10:27

we take all of it, the

1:10:30

SQL performance will decrease,

1:10:33

right? So we make a query the result

1:10:35

will be very long. So, for example, if

1:10:38

we want to take the entire column but we

1:10:41

only want to take that example, we

1:10:45

can use the syntax called

1:10:47

limit.

1:10:49

So here I will write a query

1:10:52

like this.

1:10:56

For example, if we only want to take 10, then

1:10:58

we write the limit as 10. And for

1:11:01

this SQL syntax, it's not that rigid

1:11:05

. We don't have to write long

1:11:07

to the right. For example, I want to write select

1:11:09

stars from transactions like this, that's

1:11:11

possible. Or, for example, if I

1:11:14

want to go down like before to make it neater,

1:11:20

both can work in the

1:11:22

same way. Here we will try to be

1:11:24

scary with the addition of the syntax limit of

1:11:28

10.

1:11:31

Okay, that's it. So the data that comes out is only

1:11:34

10. If, for example, we didn't

1:11:36

use this,

1:11:39

the data would still come out up to 50 or the

1:11:41

entire data. And if we

1:11:44

only want to take 10 rows, we can

1:11:47

use a limit of 10

1:11:50

.

1:11:54

Okay, next. Here, for example, after we

1:11:58

see all the data, it turns out we

1:12:00

don't want to take it all. we just want to

1:12:02

take a few columns. For example, we

1:12:05

only want to know the customer's name. So

1:12:08

here I can call the customer's name.

1:12:12

We remove the limit because we want to

1:12:14

take all ee so we can

1:12:17

just say what the column name is.

1:12:20

Then we will run. Well, it will

1:12:23

come out straight away if, for example, the

1:12:25

customer's name is Budi Santoso, Siti

1:12:28

Aminah, Andi Wijaya and others

1:12:32

like that. Then, for example, we can also

1:12:38

take ee apart from the customer name, we want to

1:12:41

take two columns at once, right?

1:12:43

So the customer name is also the product category,

1:12:47

this is the product category. Well, usually in

1:12:49

SQL, when we start typing the

1:12:52

column name, it will also

1:12:55

give recommendations about what column

1:12:59

we should write.

1:13:01

Usually we can continue writing

1:13:04

or we can also just select

1:13:06

it and it will also be written

1:13:08

here immediately.

1:13:10

Then we will run this.

1:13:13

Well, it immediately comes out as the customer's name

1:13:16

and also the product category. So from

1:13:19

this transaction data, we can see that,

1:13:21

for example, Budi Santoso bought

1:13:24

electronic products, while Siti Amina bought

1:13:26

clothing products, Andi Wijaya bought

1:13:29

hobbies, and so on.

1:13:34

Next

1:13:37

we can also write this like this.

1:13:42

It doesn't have to be to the side, but it can also be

1:13:44

downwards. For example, if I have

1:13:47

a lot of columns, I usually

1:13:49

prefer to go to the bottom because it will be

1:13:51

easier to see. And if,

1:13:54

for example, we have five columns like that

1:13:56

, then this can move to the right and the right, it

1:13:58

can be covered on the screen. we don't

1:14:00

see all the complete queries like that.

1:14:03

So, I prefer to go down and I

1:14:05

also recommend my friends

1:14:08

to write like this. So

1:14:10

actually, SQL doesn't

1:14:12

have any rigid rules

1:14:16

for writing. Whether we want to continue to the side

1:14:17

or continue to go down is up to us.

1:14:20

But from me, I

1:14:23

recommend that, friends, you

1:14:26

start getting used to

1:14:28

writing neat syntax, even from just learning. So, for

1:14:30

example, if we are running and suddenly

1:14:33

we get a long time and then we run and

1:14:35

suddenly there is an error, we can more

1:14:37

easily find where the error is. Is

1:14:39

there a [clears throat] typo or is there a comma

1:14:41

missing. It's easier if

1:14:43

we write in a good and

1:14:46

neat format. For example, here I

1:14:49

write only the customer's name.

1:14:51

I wanted to get the customer name and

1:14:53

product category but I forgot to put a

1:14:55

comma. Now. [clears throat]

1:14:58

Oh okay. Sorry, if it's like this, it will

1:15:01

still come out.

1:15:04

I'll continue first. So, for example, we want to

1:15:06

take the customer name and

1:15:08

product category, but we want to change the name.

1:15:13

So we don't want the customer's name to

1:15:15

remain written in the table as the

1:15:17

customer's name. We want to give him

1:15:20

column names like that. So we just want to change it

1:15:22

to the name.

1:15:24

Then, if we want to change the product category,

1:15:27

we just need to change the category.

1:15:30

Here we can use the ee syntax S. So S

1:15:34

means alli, right? So the

1:15:37

customer name as the name and also the

1:15:39

product category as the category. We'll

1:15:42

try this and he'll come out. What comes out is

1:15:46

no longer the customer's name here, no

1:15:49

longer the product category, but just the name

1:15:51

and category.

1:15:54

And also, if we don't

1:15:57

want to use S, it can be set as the

1:16:01

default. If we take one

1:16:03

column and then next to it there is

1:16:08

another column name and without a comma, it will automatically

1:16:11

assume that the second column name we

1:16:14

wrote is the name of the alliance

1:16:17

that we called first

1:16:19

as long as there is no comma, right?

1:16:22

We can also run this and the result will be the

1:16:24

same, still the name and category. So we

1:16:28

can use S here, we can use S.

1:16:31

You can also not use S, the result

1:16:33

will be the same. His SQL can already

1:16:38

understand what we mean.

1:16:41

So what if, for example, we want this

1:16:44

to be

1:16:46

two words, for example, full name

1:16:51

and here the purchase category.

1:16:54

For example,

1:16:56

if we want to name a column

1:16:58

with two words, we can't

1:17:02

write it like this, there can't be

1:17:04

spaces. Later it will give an error.

1:17:07

This is the error, it's almost a complete error.

1:17:10

Because it detects there is a space here.

1:17:12

So if we want those two words, we

1:17:14

can use underscore

1:17:18

so that they remain connected as one

1:17:20

word. Well, it worked.

1:17:23

So we change the customer names to

1:17:25

full names and we change the product categories

1:17:27

to purchase categories.

1:17:30

But if, for example, we want the

1:17:33

table name to be neat when we take the data,

1:17:37

it's good, for

1:17:40

example, so if, for example, we want to

1:17:42

download it directly and then, for example, we

1:17:43

want to share it with someone, to our team, to our superiors,

1:17:46

it's neat and we don't need to

1:17:48

edit it again in Excel,

1:17:50

for example, we can also use quotes.

1:17:54

So if we want to use spaces, we

1:17:56

have to use quotation marks. It's the

1:17:59

same here too. So the full space name

1:18:01

and then the purchase space category

1:18:04

this cycle. Well, then he

1:18:07

can output the column name with

1:18:11

spaces like that. It's

1:18:15

still safe so far, friends.

1:18:21

Okay, I want to and I will continue to the

1:18:25

next syntax.

1:18:27

If we had previously selected ee there are many

1:18:29

columns like that, for example here there are two columns like that

1:18:31

. Now we

1:18:34

want to take for example the name of the customer ee

1:18:39

purchase category from

1:18:41

our ee transaction data, right? So if

1:18:43

we look at the purchasing categories here,

1:18:45

there are electronics, Budi

1:18:48

bought electronics, Siti Aminah bought

1:18:50

clothes. But we actually want to see

1:18:52

what is available in our data,

1:18:54

just the purchase categories, and

1:18:57

what are there? So we only

1:18:59

look at the product category without looking at

1:19:03

who bought it. we can

1:19:06

use distinguishing.

1:19:09

So, this distinguishing feature will output

1:19:12

unique data from the column we

1:19:14

select.

1:19:16

If we look here,

1:19:18

there are still two electronics, two clothes,

1:19:21

two or more. If, for example,

1:19:23

we use listing, the result will be

1:19:25

like this.

1:19:27

So, it

1:19:30

turns out that there are only four product categories in our transactions,

1:19:32

namely electronics, clothing, hobbies, and

1:19:35

furniture

1:19:36

. We can also do the

1:19:40

same. For example, we want to see the

1:19:42

name, the name of the customer.

1:19:45

This is also the same cycle and we get a

1:19:48

unique ee, a unique name from the buyer.

1:19:55

Okay, next

1:20:00

we'll try [clears throat] to go back and

1:20:02

get the overall data.

1:20:06

Sorry, the shortcut is shift shift enter

1:20:09

not control enter.

1:20:12

Okay, here, for example, we want to see

1:20:18

transaction data that only comes from the

1:20:25

western region, for example.

1:20:29

We can do that using

1:20:31

SQL with syntaxware.

1:20:35

So, the function of this syntaxware is to

1:20:38

provide a filter to the data that we

1:20:40

retrieve, like that.

1:20:43

For example, we wanted to see the origin

1:20:46

of transactions originating from the

1:20:48

western region, so we have to define where the

1:20:52

origin of the region is the same as if

1:20:57

we had to write a filter ee to provide a

1:21:01

data filter that matches the

1:21:03

data type. So if the origin of this region

1:21:05

contains varcar, then we have to

1:21:09

write it in quotation marks.

1:21:12

because it is text, it is

1:21:14

in the form of farchar. We will run.

1:21:19

Okay, here we use the

1:21:22

single quote.

1:21:25

Well, all the data

1:21:29

coming from the western region will come out.

1:21:33

How many are there? We can see. There is Si

1:21:36

Budi, there is Dewi, there is Joko Widodo who

1:21:39

are consumers from the

1:21:42

western region.

1:21:44

For example, we can also see people

1:21:48

who buy more than one, where

1:21:53

buying more than one means the number of

1:21:54

items is

1:21:56

more than one, so it will come out, we

1:21:59

can shift enter, the result is this. So

1:22:02

who is Budi? It turns out he bought

1:22:04

more than one item, he bought two.

1:22:07

Eko apparently bought four wooden chairs

1:22:10

.

1:22:12

So, we can use this where for

1:22:14

all data types that exist in

1:22:17

our data, like that.

1:22:20

Then

1:22:22

we can also use this

1:22:26

multiple times, right? So it's not just

1:22:29

one category, but for example, I want to

1:22:32

take a transaction originating from the west

1:22:36

with one item. So earlier

1:22:39

we already had a filter for the number of items

1:22:41

more than one, then we will

1:22:43

add the query N. N number eh sor n

1:22:48

origin region is the same as barokaran.

1:22:54

Well, these are ee three transactions that

1:22:57

come from the western region and the number of

1:22:59

items is ee not more than one

1:23:02

.

1:23:06

Then, eh, there are a lot of syntaxes in this place,

1:23:09

huh? Like this, there is where

1:23:13

and then we also have something called

1:23:15

or.

1:23:18

Friends, you understand that there is no difference between that and the

1:23:20

same. This is actually the same way it

1:23:22

works in logic and mathematical logic

1:23:26

. So there is N and OR ee it's the same

1:23:29

ee how it works here. For

1:23:32

example, if N means ee the first filter

1:23:36

must be true, must have ee

1:23:41

must be true and the second filter

1:23:42

must also be true, right? So if N

1:23:45

must fulfill both, whereas if

1:23:48

OR H can fulfill only one. So

1:23:52

where the number of goods is more than one or the

1:23:56

region of origin is from the west. We

1:23:58

will try this. Well, this is the result.

1:24:02

If it turns out that Budi fulfills

1:24:04

two criteria. He's from the west and

1:24:07

the number of items coming in is more than one.

1:24:09

That's why the first data came out.

1:24:12

Then the second one here is ee, the

1:24:16

area of ​​origin is from the south

1:24:18

but the number of items is more than one.

1:24:20

This means that if he meets one of

1:24:22

the criteria, he will still be

1:24:24

out because here we use OR. Oh, I see

1:24:30

. And if N is the example,

1:24:33

we have to fulfill both,

1:24:36

namely the one that comes from the west and the number of

1:24:38

items is more than one.

1:24:43

Okay.

1:24:45

Then

1:24:50

for where this is also still there.

1:24:53

Earlier, we filtered it for

1:24:56

numeric data types, and also for

1:25:00

text data types. Now, what if,

1:25:02

for example, we want to filter ee based on

1:25:07

names that contain certain words

1:25:12

. For example, here I want to take

1:25:15

the names of customers that contain the word Budi,

1:25:19

we can use like.

1:25:22

So we can choose not only not

1:25:25

have to write like the full name. For example, the

1:25:27

customer's name is the same as Budi

1:25:30

Santoso.

1:25:34

This can be

1:25:36

this can cycle and the results will

1:25:38

come out. But we can also, for example,

1:25:40

only take the ones that contain the

1:25:43

word Budi. If so, we

1:25:45

can use like with

1:25:49

ee what is this use of ee?

1:25:53

That's the percent sign, right? So we can write it like

1:25:56

before, we have to write it in quotation marks

1:25:58

first then we add the percentage

1:26:01

at both ends and the name. This is

1:26:04

the cycle where the results come out with Budi.

1:26:09

So there is only one

1:26:12

customer name that contains Budi from

1:26:14

our transaction data.

1:26:16

Okay,

1:26:19

I'll continue. Maybe I'll be a bit

1:26:21

quicker as we don't have

1:26:23

much time left.

1:26:27

Then for the next syntax it

1:26:30

exists.

1:26:33

Okay, let's go back to

1:26:35

the table first. Let's take a look at the table first.

1:26:38

Here there is ID, date,

1:26:43

status.

1:26:46

OK, for example, if we want to see the

1:26:51

transaction data

1:26:54

from the most

1:26:57

purchases to the smallest,

1:27:00

we can sort the data

1:27:02

using a syntax called order

1:27:04

buy.

1:27:06

So, this buy order will sort

1:27:08

the data based on the column we

1:27:10

choose, like that. Since I wanted to see

1:27:13

ee based on the most purchases

1:27:16

, that means we will try to

1:27:19

use the number of items here.

1:27:21

We order based on the quantity of goods.

1:27:23

Let's try, run.

1:27:27

The result [clears throat]

1:27:27

comes out from here from ee the smallest number of items

1:27:31

first. One. Then

1:27:34

we can see that the most are

1:27:36

in the top 10. So, what if, for example,

1:27:40

we want to take ee but it's the other way around, we

1:27:44

want the largest one to be the

1:27:45

top one, and we can use the

1:27:49

syntax des.

1:27:51

So by default, for example, if we

1:27:54

use order by in SQL, it will be

1:27:57

applied automatically, eh, reading as ASC

1:28:01

or ascending. Ascending is in order

1:28:03

from smallest to

1:28:05

largest. So if we don't set

1:28:07

anything here, we don't write it,

1:28:09

it will automatically sort from

1:28:11

smallest to largest. But if for example we

1:28:14

want it from largest to smallest, we

1:28:17

can use the syntax des which means

1:28:20

descending. Well, if we get the results

1:28:23

like this. So the top 10

1:28:26

then 5 then 4 until finally the

1:28:29

number of items is one,

1:28:32

right?

1:28:34

Then, this order by is not

1:28:38

only something we can use for

1:28:40

one column, but also for more than

1:28:42

one. For example, we want to see ee ee mm

1:28:49

data that is sorted ee based on the number of

1:28:51

items on each

1:28:55

date, right? So for example

1:28:59

here we will select the date

1:29:02

then the number of items

1:29:05

if the cycle is

1:29:10

oh sorry what was it called earlier, the

1:29:15

transaction date, sorry

1:29:18

ee, this transaction cycle, then it will be

1:29:21

in order of date but it will

1:29:23

sort from the largest

1:29:25

number of items to the smallest,

1:29:27

this is the transaction date one, there is a quantity of

1:29:30

1 du. Then, for example,

1:29:34

on the 11th, there were also

1:29:37

purchases of two or three

1:29:40

items.

1:29:43

Okay,

1:29:45

then

1:29:48

here for writing order by it's

1:29:51

not only ee we have to be so we

1:29:55

have to select the column but we don't

1:29:57

always have to write the name of the column

1:29:59

. For example, it's like this.

1:30:02

So, for example, we want to take specific

1:30:06

columns, for example, only the

1:30:08

transaction date, then the same product category,

1:30:12

then the number of items.

1:30:15

Then we want to sort by

1:30:18

transaction date only, we can

1:30:21

choose that, we can write

1:30:24

sort, we can write like this. Order by the

1:30:27

date our transaction runs. It will be

1:30:30

sorted by transaction date

1:30:33

but we can also run it by

1:30:36

selecting by writing one. So

1:30:39

what does this one mean? This one means

1:30:42

that we choose order by the

1:30:44

first column.

1:30:46

So the first column here is the

1:30:48

transaction date. Then it will be

1:30:50

sorted by transaction date.

1:30:52

Then

1:30:55

we try to get used to writing neatly first,

1:30:57

sorry. Then we try, for example, if we

1:31:00

want to sort by the number of

1:31:03

items, we can write the number of items

1:31:08

or we can directly select the number of

1:31:10

items here, which is the 1st 2nd 3rd column

1:31:14

that we write in select, then we can

1:31:16

select the order by 3. Then, for example,

1:31:19

I want to sort it from the

1:31:21

largest, so the order by 3 is descending.

1:31:24

Well, this is the result. So, the items with the

1:31:27

largest number of items immediately

1:31:29

appear at the top, right?

1:31:34

Let's continue, okay?

1:31:37

So next

1:31:39

in SQL we can also do ee

1:31:43

aggregation like that, right? So

1:31:45

what does aggregation mean? What is meant by that is the

1:31:47

syntax of

1:31:51

mathematical operations such as count or

1:31:54

calculating.

1:31:55

Then there is also sum,

1:31:58

then there is also average,

1:32:01

there is min, and there is also max. I'll try to

1:32:06

write the syntax first. Here,

1:32:09

for example, if we want to count the number of

1:32:12

data, we can select count ee

1:32:16

star.

1:32:17

Then for SAM, we can, for example,

1:32:21

add up the unit prices

1:32:26

or just add up the number of items

1:32:28

. Then we

1:32:30

add up the average unit price. Eh, sorry

1:32:33

I pressed the unit price. Then for the

1:32:36

minimum, for example, we want to see the total

1:32:39

payment.

1:32:41

We can also see the total number of buyers,

1:32:44

which we can do all in

1:32:47

SQL. And if we want to

1:32:49

take it all at once like this, don't forget

1:32:52

to use a comma. So we have to

1:32:55

put a comma in each column we

1:32:57

take.

1:32:59

This is if we try.

1:33:02

Oh sorry, I didn't use it in the end

1:33:03

.

1:33:05

Well,

1:33:06

the count of all the star counts comes out

1:33:09

or we count all the rows of data,

1:33:11

there are 50. Then the same is the number of items. The

1:33:15

number of items purchased was 91. The

1:33:18

average unit price or average

1:33:20

price of each item was Rp.

1:33:22

280,000

1:33:25

and the smallest total payment was Rp.

1:33:27

75,000

1:33:29

and the largest was

1:33:31

Rp. 1,200.

1:33:35

Then, for example, if we want

1:33:37

the writing to be neater,

1:33:38

we can also use allas like before

1:33:40

. So we can just

1:33:42

give it a name, for example, we name the amount of our data

1:33:46

as the amount of data, then ee, the

1:33:50

total number of items,

1:33:52

we want to change it directly to the total number of

1:33:54

items, the

1:33:56

average unit price, for example, we want to

1:33:58

write the average price,

1:34:02

min total payment, for example, is the

1:34:06

smallest purchase.

1:34:11

And finally, Mas, the total payment

1:34:13

is the largest purchase.

1:34:16

Don't forget,

1:34:18

underse. This is also possible and the results

1:34:21

are immediately neater, yes,

1:34:24

for the writing format, ee, the

1:34:28

columns like

1:34:30

that.

1:34:32

This is also the same if, for example, friends

1:34:33

don't want to use S, they can.

1:34:36

we can just run it straight away. The results are

1:34:39

also the same. It seems like nothing has changed

1:34:40

because the results are the same,

1:34:42

right, friends?

1:34:44

But here we will try to get used to

1:34:45

using S first so that friends don't get

1:34:47

confused. Okay. So, next, besides

1:34:52

aggregation like the one above, we

1:34:55

can also do ee aggregation

1:34:58

based on columns, right? So, for example, if

1:35:02

we want to see ee, we can see the

1:35:06

total data for the whole

1:35:09

table. When we use

1:35:10

the star, we take all the ee rows

1:35:13

in this table. Now we want to see the

1:35:17

entire line but based on the ee

1:35:20

region of origin. So what does that mean? So,

1:35:22

we want to see, for example, in the west,

1:35:25

from the western region, how many transactions there are,

1:35:28

we can count the stars. So we

1:35:32

select the region of origin then count the

1:35:34

stars and for example, if we want to

1:35:37

see the total purchase, we

1:35:40

add S to the total payment.

1:35:45

Okay, so it's nice, let's give it a name

1:35:49

as total transaction. the

1:35:52

transaction amount

1:35:58

and also the total payment amount as the

1:36:01

payment amount.

1:36:04

Well, we can do this. But there is

1:36:08

one addition. If, for example, we want to

1:36:10

see based on the region of origin, ah,

1:36:12

we need one more syntax that

1:36:16

we can call group buy. So,

1:36:19

this buy group means that we want to

1:36:21

group the number of transactions and also

1:36:25

the amount of payments based on the

1:36:27

region of origin. So, we want to group

1:36:29

the aggregations performed in this syntax

1:36:32

based on the columns we select.

1:36:38

This is if we try. Well, the result

1:36:41

will be like this. So, in the west there are 10

1:36:43

transactions. Oh, it turns out they're all the same.

1:36:46

In the west, center, south, cucumber. East

1:36:49

or north, there are 10

1:36:50

transactions in total. This means our transactions

1:36:52

are spread evenly, right? Then ee,

1:36:56

but if we look at the

1:36:57

payment amount, it is not the same in each

1:36:59

region. The largest one is in

1:37:02

the central region and the smallest one is

1:37:04

in the northern region.

1:37:08

Then we can also add a

1:37:13

buy order. So, if we use group buy,

1:37:16

we can group

1:37:19

the groups based on the region of origin and

1:37:21

we can freely choose the buy order.

1:37:25

So we want to sort it based on the

1:37:28

order here, the region of origin. If it is

1:37:31

based on the region of origin, it means it

1:37:34

will be sorted alphabetically,

1:37:36

right? But if for example we want

1:37:38

something else, for example the number of transactions is the

1:37:40

same, we can just choose

1:37:42

order by 2. But because here the number of

1:37:45

transactions is the same, so I want to

1:37:47

sort it based on the

1:37:49

payment amount only. So I'll choose

1:37:51

column 3 like that. So we'll see

1:37:54

data like this. I'll try it first.

1:37:58

So the order is no longer based on

1:38:01

region of origin but based on the amount

1:38:04

of payment. Because we have selected

1:38:06

order by 3. So this is still valid, right?

1:38:09

One 1 2 3 will be based on

1:38:12

our choice in silect. The order

1:38:14

we choose is selected.

1:38:17

Because I want to sort by

1:38:19

payment amount I choose three. And because

1:38:22

I didn't write anything here

1:38:24

ascending or descending, it will

1:38:26

automatically sort from the smallest. The

1:38:28

smallest is at

1:38:31

R2,435,000.

1:38:34

RIB is in the northern region and the largest one,

1:38:39

R million, is in the central region, right?

1:38:44

Then, for

1:38:47

example, if I want to do this, we can

1:38:50

see all the data, all the origins of the region,

1:38:53

right? For example, if I only want to

1:38:55

filter the largest region from the

1:38:59

largest region, ee not the largest, sorry,

1:39:02

the one with the most payments, the

1:39:05

syntax is also the same as this. But

1:39:08

we can add one more syntax,

1:39:10

namely limit.

1:39:12

Because we only want to see

1:39:14

one largest area, we can

1:39:17

choose limit 1. So it will choose the

1:39:22

largest ee.

1:39:23

Hey, just the biggest one. Because

1:39:26

we want to start from the largest one,

1:39:28

don't forget to add descending order,

1:39:30

then we run it.

1:39:33

Only one data came out because

1:39:35

we limited the data earlier. If we remove the limit,

1:39:40

it will all come out. But if

1:39:42

we want to see only one, we can

1:39:45

increase the limit by one so that only one data will come out

1:39:48

. This can also be limited to more than just

1:39:50

one. For example, we want to see the top

1:39:52

three, the top three regions based on

1:39:54

the number of payments. we can use a limit of

1:39:57

3 and the result will be three

1:39:59

data with the largest payment amount

1:40:02

. three areas with the

1:40:04

largest number of payments

1:40:07

like that.

1:40:11

Okay,

1:40:15

maybe that's all from me for the practical session,

1:40:21

ee, that's all for now. Looks like

1:40:24

our time is up too,

1:40:27

Mr. Tika. That's

1:40:31

absolutely right, Sis. So, does Ms. M

1:40:34

Hana have anything else she would like to add

1:40:36

?

1:40:38

Okay, maybe I'll

1:40:42

add a little something to conclude. So,

1:40:44

from this practice, from the material and

1:40:47

practice, we have learned, starting

1:40:49

from the definition of SQL, and also

1:40:52

data types. We have also tried to

1:40:55

practice the basic concepts of SQL, right?

1:40:58

From SINTAK Select, there is where, there is

1:41:00

Orderby and many

1:41:02

other SINAKs. Eh, I hope that this material

1:41:06

can be useful and that it can be beneficial

1:41:10

for all my friends. Oh, and also,

1:41:12

if, for example, after

1:41:14

this class, my friends feel that there's a lot of

1:41:17

SQL that needs to be learned,

1:41:19

that's very normal. Hey, just relax,

1:41:21

just try to practice it slowly because

1:41:23

learning SQL requires

1:41:26

a lot of practice so that you

1:41:27

can get used to writing scripts or

1:41:29

writing queries. The important thing is that

1:41:32

friends already understand the basic pattern first

1:41:33

. Later, once you understand the

1:41:36

basic patterns, it will be easier for

1:41:38

you to learn

1:41:39

more advanced queries. Ee maybe that's from me

1:41:42

. Thank you very much, friends,

1:41:44

for your attention and also for

1:41:46

your time. Hey, for now I'll

1:41:48

return it to Kak Tika. Thank You.

1:41:51

Okay, thank you too Sis. E so ee

1:41:54

a little explanation too, maybe some of my

1:41:56

friends are still

1:41:57

wondering, "Sis, how does the

1:42:00

minimum work work?" So,

1:42:02

I'm going to share my screen with you guys for

1:42:03

a little explanation before we get into the

1:42:06

Q&A session using Slido. So,

1:42:09

perhaps since earlier,

1:42:12

quite a lot of friends of Kak Hana have been

1:42:13

asking questions in the chat column and maybe

1:42:16

the questions that I didn't get a chance to

1:42:18

see earlier are really welcome to be thrown

1:42:20

to Slido because later there, if

1:42:22

your friends' questions are sufficient

1:42:24

or representative enough, please like them

1:42:26

so that the question appears at the top

1:42:28

and I can get more priority.

1:42:31

Well, I will explain a little technically

1:42:33

how to do the minimum,

1:42:35

Sis? First, my friends,

1:42:37

I have shared this link earlier in the

1:42:39

chat column. Please just download it. Later

1:42:42

here, the owner's name is just write

1:42:45

your name. For example, Kasmurangkir or Ahmad

1:42:48

Dani or whatever your friends will

1:42:50

use the name for. Then, for the

1:42:52

first instruction,

1:42:54

this document is not open access, so download it

1:42:56

first and then work on

1:42:59

each device. Then you

1:43:01

can do the post-test

1:43:03

first. And for today's class recording,

1:43:06

it's at point number two. Here at

1:43:09

point number,

1:43:10

you can access the data set

1:43:13

that we used for practice.

1:43:15

Maybe someone missed it and

1:43:17

wants to repeat it through the recording, that's totally

1:43:19

fine, you can practice it after

1:43:21

this class. Well, then later

1:43:24

after you upload the results of your

1:43:27

friends' work on the template on one of

1:43:29

your social media, what is uploaded can

1:43:30

be a PPT file or a screenshot file

1:43:33

from this file except for slide 2. So, you

1:43:35

don't need to share this work instruction slide

1:43:38

on your social media, friends

1:43:39

. Well, don't forget to

1:43:43

tag #learnmyscale later. So, for the

1:43:46

post link or post link and

1:43:49

the tuon, you can e up or

1:43:53

enter it into this form, number

1:43:55

5, no later than Wednesday, July 15th

1:43:58

at 23.59.

1:44:00

Well, later you can fill this in

1:44:02

with a course summary or

1:44:05

brief explanation of what you received today

1:44:08

. You can enter it

1:44:10

here and in the right column, you can

1:44:12

put your name later. Well, the

1:44:16

case study example is also at

1:44:18

this point, read it carefully and then

1:44:21

work according to the instructions.

1:44:23

Later, you can include the results of processing the data set on SQL

1:44:26

Light Online on this slide,

1:44:28

it can be in the form of a

1:44:30

screenshot or something like that.

1:44:32

Well, then later, after

1:44:35

you have practiced, fill it in with the

1:44:38

results of your analysis that you have

1:44:39

done in the online SK on the following slide

1:44:42

. Well, that's it. Easy, right friends? It's

1:44:45

easy because my friends understand how

1:44:47

to do the minimum work. And

1:44:50

next we will continue to the

1:44:52

question and answer session, Sis. We still have

1:44:54

about 7 to 10 minutes left.

1:44:57

So, perhaps there are questions that have

1:44:59

not been answered previously in the

1:45:01

chat column, we can ask them directly in the

1:45:05

Slido application. So, friends, you can

1:45:08

now open the Slido application

1:45:11

or the Slido website, which I have

1:45:13

also shared. I will share it here.

1:45:19

Well, if the question is

1:45:21

representative enough, please like it,

1:45:22

friends. because it is really not

1:45:25

possible for the admin to scroll

1:45:27

down. So, for example, if he

1:45:28

represents, just like it like this.

1:45:30

If you press like, it will appear at the

1:45:33

top. So I'll start with the

1:45:35

most liked questions

1:45:37

to make things easier. Okay, let's get

1:45:40

straight to it, Sis, there's a question from

1:45:43

Anonymous.

1:45:45

Hello, Sis. I want to study

1:45:47

further for SKL. For beginners,

1:45:50

which SQL is better to install? My SQL or HG

1:45:54

Gray SQL yes. Okay, Sis Hana can answer

1:45:57

.

1:45:58

Okay, for beginners,

1:46:00

both of these are actually very beginner

1:46:03

friendly in my opinion. So,

1:46:05

just adjust

1:46:08

the specifications so that they are compatible with

1:46:10

your laptop. Ee so ee

1:46:13

both of them are beginner friendly. So just use the one that's

1:46:16

most comfortable for you

1:46:19

because the syntax is also

1:46:23

[snort] SQL, the syntax in it

1:46:25

will be more or less the same and won't

1:46:27

be much different. So you don't

1:46:29

need to think about which one to learn from

1:46:31

first, just learn the most comfortable one first. Later,

1:46:33

once you're fluent, it will be easy to

1:46:36

move to other SQLs.

1:46:41

Okay, I hope that answers your question, Anonymous.

1:46:43

We're next, Sis. On to the

1:46:45

next question.

1:46:46

Hey, there's another question from Anonymous.

1:46:51

What is the difference between using Python and SQL? Is Python

1:46:53

a data engineer and SKLU

1:46:56

or data analysis or both of them

1:46:58

use what data as analysis? This is a

1:47:01

half-hearted question. But

1:47:02

I hope Sis Hana understands, Sis.

1:47:04

Can you answer, Sis Hana?

1:47:07

Okay. Okay. Eh, so SQL and Python have

1:47:10

different usages, huh? If there is a

1:47:12

lot of Python in Python, it is more

1:47:16

advanced. He can start from ee

1:47:19

pulling data, exporting data, processing

1:47:22

data. There's a lot more that can be

1:47:24

done in Python. Then in Python you

1:47:28

can also do what's it called, like that

1:47:32

. So taking SQL queries or

1:47:35

running SQL in Python like that. Meanwhile,

1:47:38

SQL is usually only for

1:47:43

databases. Whether it's

1:47:46

ee making ee creating data, then also

1:47:49

processing data, accessing data like that. So,

1:47:55

in terms of tools,

1:47:58

Python is more advanced and more complex. Ee

1:48:00

continues to also work as a data

1:48:03

and SQL engineer. Actually,

1:48:06

both data engineers and SQL,

1:48:08

or both data analysts can

1:48:10

use SQL or

1:48:13

Python, depending on

1:48:17

what they want to do. Ee

1:48:20

so it's not actually limited, data

1:48:22

analysis can also be done in

1:48:24

Python, eh, it can also be done in SQL.

1:48:27

But eh SQL is specific only

1:48:30

for the database.

1:48:35

Hopefully the answer is yes.

1:48:37

Okay, I hope that answers your question, Anonymous.

1:48:40

Let's move on, Sis, to the

1:48:41

next question, which is still from Anonymous. Sis,

1:48:45

if you are a beginner, can you practice on your own every

1:48:47

day? So

1:48:49

what data can be used for the practice example, Sis?

1:48:51

Okay, what might the data set look like,

1:48:53

Sis? You can answer, Sis Hana.

1:48:56

Okay, for beginners, you can definitely

1:48:58

practice every day. Nowadays,

1:49:01

internet access is very easy

1:49:03

, there are so many examples of

1:49:06

exercises, like the ones My Skill provides, there

1:49:09

are also free short classes,

1:49:11

and there are also examples of SQL exercises from YouTube or

1:49:15

other social media, and there are so many

1:49:17

examples of

1:49:20

databases, and there are so many

1:49:24

kinds of

1:49:26

databases on the internet, and for example,

1:49:29

eh, I usually use a website for exercises

1:49:32

, it's

1:49:35

called kegle K A G.

1:49:38

There are one or two gs. That

1:49:41

can be used for ee, there are

1:49:44

lots of ee databases available there, from

1:49:47

free to paid ones, which

1:49:49

you can use for practice, and the

1:49:52

data there is

1:49:54

usually quite similar to real data,

1:49:57

right? There is transaction data or there is hospital ee data, there are

1:50:01

lots of different

1:50:03

types. So you can try

1:50:05

exploring there and can try practicing

1:50:07

using that

1:50:09

from the name Kegel.

1:50:12

Okay, I hope that answers it, Sis.

1:50:15

Maybe it will be very useful later. Is

1:50:18

this a website or an application, Sis?

1:50:20

Hey, there are lots of them on the website,

1:50:23

they are available on the website.

1:50:26

Later, friends, you can explore Kegle, that's what it's

1:50:28

called, Sis. Maybe later we will.

1:50:32

Okay, next time, Sis. There is

1:50:34

another question from Anonymous. Sis, do you usually

1:50:38

use SQL or Python more often in the work world

1:50:40

? Hana can do it.

1:50:43

Okay, maybe it's different, depending on the profession

1:50:46

too. His profession too, yes. So, for

1:50:49

example, if you are a data analyst, you will probably

1:50:51

use SQL more often because

1:50:54

your focus is usually only on

1:50:57

data collection and data analysis,

1:50:59

data processing. But for

1:51:01

example, data scientists or

1:51:04

data engineers usually

1:51:07

use both, but more

1:51:09

often Python because they are the ones who

1:51:11

manage the data, they are the ones who

1:51:14

flow the data, they are the ones who

1:51:16

manage the database. Then also,

1:51:20

eh, data scientists usually

1:51:22

use Python to create models

1:51:25

, machine learning models. So it

1:51:28

depends on the profession.

1:51:32

Okay, I hope that answers your question, Anonymous.

1:51:34

Then next, Sis, to the

1:51:37

next question.

1:51:40

So, Sis, if you want to learn about

1:51:43

language, comment or query language,

1:51:45

what are the tips or where can you learn

1:51:48

? You can answer, Brother Han.

1:51:51

Okay. So, if you want to learn, you can

1:51:54

use the example we

1:51:56

gave earlier, practice together like that

1:51:58

. Just use the free online ones first

1:52:00

, then also look for data sets. The data

1:52:03

set is also the same, just look for a free one first

1:52:05

. Then we try to do

1:52:07

lots of practice. So, ee,

1:52:10

if for example writing this query is

1:52:12

the most effective way,

1:52:15

we have to practice a lot, try

1:52:17

out lots of questions like that, okay? So,

1:52:21

this SQL query is usually

1:52:23

used to answer questions.

1:52:26

What do you mean by answering the question? So, for example, like the one earlier,

1:52:28

if I were practicing, I

1:52:30

would ask, "We want to see

1:52:31

what products are the most purchased,

1:52:33

for example, or we want to see

1:52:37

which region has the

1:52:39

most purchases, and also what are the

1:52:42

most purchased products in that region, for example,

1:52:44

just practice a lot

1:52:46

with case examples that are

1:52:49

close to the relief, close

1:52:52

to the case examples that are usually used in

1:52:55

businesses, for example,

1:52:57

what products

1:52:59

sell the most. So,

1:53:00

that's my advice,

1:53:03

practice a lot.

1:53:07

Okay, I hope that answers it, Sis. Don't

1:53:09

forget to practice and

1:53:11

explore a lot, Sis.

1:53:13

Then there's another one from Anonymus,

1:53:16

Sis. Sis, if you want to learn more about data analysis

1:53:19

, what software can you

1:53:22

learn or is commonly used in

1:53:24

large companies, such as for

1:53:26

visualization and so on? You can

1:53:28

answer that, Sis.

1:53:31

Okay. Actually, what kind of software is used?

1:53:34

Every company

1:53:37

already has the software they

1:53:39

use. But you

1:53:42

don't need to worry about what software.

1:53:44

Because usually the software

1:53:46

works in a similar way. That's it. So

1:53:48

as long as you understand and can

1:53:53

query in one software,

1:53:56

usually moving to another software

1:53:57

will be very easy. You just

1:54:00

need to adjust to using the

1:54:02

software. Then,

1:54:05

for visualization, it's also very similar.

1:54:08

So, as long as you're good at it, just try to

1:54:11

learn how to make

1:54:13

charts, make tables, and charts,

1:54:16

for example, there are P charts, bar charts

1:54:19

, try to learn how to make tables and

1:54:21

charts first. Later,

1:54:24

you will definitely follow how to use the tools

1:54:26

. So you don't need to

1:54:28

worry about which software to use.

1:54:33

Okay, the important thing is to explore the

1:54:35

methods first so you understand better

1:54:37

, okay, before using

1:54:39

more advanced software. Okay, I hope that answers

1:54:42

it, Anonymous. Let's move on to the

1:54:44

next question, okay?

1:54:49

Sorry for the comma usage, do you

1:54:51

use a comma at the end of every word

1:54:53

? You can answer, Han.

1:54:56

Okay. So, not at the end of every word

1:55:01

. So, for example, if we take

1:55:03

or select data, select

1:55:06

columns, for example, we take column A,

1:55:09

column B, column C.

1:55:12

We have to put a comma after every column name.

1:55:16

So, we use commas in

1:55:20

all the columns we select, but not

1:55:23

in all the syntax. So, for

1:55:26

example, if we select A B C from transaction,

1:55:30

for example, in the from transaction,

1:55:32

we don't need to add a comma again,

1:55:34

otherwise it will give an error. So, just stop

1:55:36

before from.

1:55:40

Okay, I hope that answers it, Anonymous.

1:55:43

Next, we'll take another question from

1:55:47

Anonymous. Is freelance data analysis

1:55:49

common, and secondly, are there

1:55:52

data analysis agencies? You can answer, Sis.

1:55:55

Okay, freelance data analysis is

1:55:59

very common because, in my opinion, this is a

1:56:02

field that not everyone really

1:56:04

needs, but the demand is still

1:56:07

high, but uh, the supply is not yet

1:56:11

sufficient, you know.

1:56:13

So, there's still a lot of potential for

1:56:16

you to become a freelance data analyst

1:56:18

because the opportunities are very big.

1:56:21

Also, for [clears throat], agencies are

1:56:24

usually more like vendors,

1:56:26

if in the data field, it's

1:56:28

usually in the form of vendors. So,

1:56:31

joining a vendor consultant

1:56:34

as a data analyst.

1:56:38

Okay, next time, Sis. I'll take a moment off

1:56:41

because the em My network suddenly got a bit

1:56:44

stuck, Sis. I'll go next, Sis. For the

1:56:46

next question,

1:56:49

it's from Anonymous again. Is data

1:56:53

analysis still relevant for the next 5 years

1:56:55

and beyond? Thanks, Sis.

1:56:57

Can you answer it?

1:57:01

Okay. Wow, I'm like a psychic. But

1:57:05

if you look at it from now on,

1:57:08

uh, what's it called?

1:57:10

This field of data analysis

1:57:12

will continue to grow because uh,

1:57:15

we always use

1:57:17

data for everything, there's definitely data, and this data is

1:57:20

very powerful. So of course it will be

1:57:23

very relevant, will still be relevant, and will still be

1:57:26

needed in the future

1:57:28

because uh, especially now that technology is

1:57:32

developing. AI is also

1:57:34

developing a lot, uh, and

1:57:36

behind it are the machines

1:57:39

[clears throat] that learn to use data

1:57:40

. So uh, for data analysis, it's

1:57:44

still very relevant for the next 5 years

1:57:47

or maybe the next 10 years

1:57:49

.

1:57:54

Okay, I hope that answers it, Sis Anonymous.

1:57:58

Then we'll go to the next one, Sis.

1:57:59

We'll take the last two questions

1:58:02

, Sis, for tonight. And

1:58:05

Sorry guys, if we

1:58:06

can't answer all your questions

1:58:08

tonight. If you really want to advance

1:58:11

in this field, you can join our bootcam

1:58:14

or we can join the

1:58:17

next class. That's it. I have

1:58:20

another question, Sis. Sis, I

1:58:21

wanted to ask, uh, in the

1:58:24

formula, there's the highest sales. Is the

1:58:27

highest sales the same as the

1:58:29

best-selling one? Can you answer that, Sis?

1:58:33

Yes, that's right. Usually, it's like that

1:58:35

if we say, for example,

1:58:38

at a shop. The simplest way is

1:58:40

to ask

1:58:43

the seller, "Sir, what's the best-selling one

1:58:45

here?" Uh, the guy or girl

1:58:48

will definitely answer which product is

1:58:50

bought the most, right? So, by

1:58:52

definition, is the highest sales the

1:58:54

same as the best-selling one? Yes, that's

1:58:57

right. So the best-selling one is the one

1:58:59

bought the most.

1:59:03

Okay, [clears throat] the definition is still the same

1:59:05

as the general definition, Sis.

1:59:07

Okay, let's go to the next one, Sis. Sis,

1:59:11

where's a good way to start learning data analysis? May I?

1:59:14

I told you, Sis. Hana.

1:59:18

Okay. Actually, there are many roadmaps for learning data

1:59:21

analysis, but

1:59:25

some people work right from the start

1:59:28

, they start directly in

1:59:30

the field of data analysis, but there are also those who

1:59:33

start from, for example, professions

1:59:36

related to data, for example,

1:59:37

in data operations. Data

1:59:40

operations usually still process

1:59:42

data, although not as ee in not

1:59:47

doing analysis like data analysts

1:59:49

. So they process data ee

1:59:54

tidy up data or also ee do

1:59:57

data cleansing ee that is still very much

2:00:01

in line with data analysis. And

2:00:03

I think that is also one of the

2:00:06

other options to

2:00:08

start. So it doesn't have to be directly from

2:00:10

data analysis, but from

2:00:13

any profession that uses data, like that.

2:00:15

Because if, for example, we have started to

2:00:18

get used to using data, have started to

2:00:20

get used to accessing, processing data, it

2:00:23

will definitely be

2:00:25

easier for us to process data until we

2:00:28

can finally find insights

2:00:30

and become expert data analysts

2:00:33

.

2:00:35

Okay, I hope that answers

2:00:37

your question, Sis. Let's take the last two questions

2:00:41

from Anonymous and

2:00:44

Prabowo Sudianto, Sis. Okay,

2:00:47

first let's read First of all, Sis. What's the

2:00:50

best application to use for syntax writing,

2:00:52

Sis? So that it doesn't get tired of

2:00:55

handwriting? You can answer, Sis Hana.

2:00:58

Okay. There are actually many for syntax writing

2:01:01

. Uh, this

2:01:04

means study notes. That's

2:01:07

my suggestion, adjust it to

2:01:10

how you usually feel comfortable studying

2:01:12

. Some people, for example, if

2:01:14

you're tired of handwriting, you prefer

2:01:17

typing. The simplest

2:01:19

is we can use Google Docs.

2:01:22

That's also free from Google. We can

2:01:25

just write in Google documents or,

2:01:27

for example, if we want to write in notes on a

2:01:30

laptop notepad, that's possible.

2:01:33

Or, if, for example, you want to use

2:01:36

more varied note-taking,

2:01:39

which is more flexible and can be decorated, there

2:01:41

's also something called Notion, which is

2:01:44

written as N O T I O Nion. That's also

2:01:48

one of the tools for

2:01:51

taking notes, whether it's material that

2:01:53

uses coding or

2:01:55

other learning materials. So,

2:01:58

just adjust it, but there are lots of

2:02:00

applications you can use.

2:02:02

Okay, so the key is, it's okay to just

2:02:04

explore it first Is that so, Sis.

2:02:07

Okay, last question, Sis.

2:02:10

Ee the question, Sis, for the

2:02:13

long term, is it better to have a large project,

2:02:16

My SQL or [ __ ] DB? You can answer, Sis

2:02:20

Hana.

2:02:21

For the long term, it actually comes

2:02:25

back to the long term, eh, it depends

2:02:28

on what the project is like. Then also

2:02:30

usually every company has what they

2:02:34

call it? Has its own SOP.

2:02:37

Do they want to use which tools

2:02:40

and later you can see which tools

2:02:42

are more compatible with which language

2:02:44

. Are they

2:02:46

using SQL or MongoDB?

2:02:49

And ee for

2:02:53

this ee also needs to be adjusted to

2:02:56

what the project is like and

2:02:58

how long the long term is. So

2:03:01

to determine that, you have to detail what

2:03:04

your needs are like. Later,

2:03:06

you can check which one is

2:03:09

most suitable for your needs,

2:03:10

then that one is chosen.

2:03:16

Okay, that question also concludes

2:03:19

our class e perk tonight. Ee

2:03:22

thank you to Sis Hana for

2:03:24

taking the time of course

2:03:26

tonight and for providing an explanation that is

2:03:28

certainly very. And I hope that

2:03:31

we can absorb this material and also

2:03:33

be able to practice to be

2:03:35

better, okay, Sis.

2:03:37

Okay, since the presentation session is

2:03:40

over, I want to invite you guys

2:03:42

as usual to open the camera first

2:03:45

because we will be doing a

2:03:47

documentation session, guys. It's

2:03:50

okay to be on cam later I will

2:03:52

count 1 to 3. You guys can

2:03:54

give your best pose

2:03:57

tonight, okay. Okay, I'll count to three. You

2:04:00

guys can smile or you can

2:04:03

give your best pose, okay. 1 2 3.

2:04:10

Okay, one more time.

2:04:14

1 2 3.

2:04:19

Okay, wait. Repeat. 1 2 3.

2:04:26

Okay, next slide. There are

2:04:29

still many, guys, on the

2:04:31

next slide. 1 2 3.

2:04:36

Okay,

2:04:38

thank you guys for

2:04:40

coming tonight. I hope you have a

2:04:42

better sleep tonight

2:04:45

and also hopefully the material presented

2:04:47

today can be useful, beneficial for

2:04:50

us so that later for every

2:04:52

career path we choose can

2:04:54

lead us to the success we

2:04:56

want or desire. Like that, okay.

2:04:59

Well, I also apologize if there are any

2:05:01

mistakes during this class.

2:05:03

And again I want to say

2:05:05

thank you to Sis Hana and have a good

2:05:07

rest, Sis. May your sleep be more

2:05:09

restful and safer and more comfortable

2:05:11

tonight. See you again, Sis

2:05:14

Hana.

2:05:16

Thank you, Sis Tika and friends.

2:05:20

And for the class tonight, I

2:05:23

would like to thank you for your enthusiasm and

2:05:24

also for my friends

2:05:26

tonight who are still enthusiastic if you

2:05:29

have any questions, you are welcome to

2:05:31

DM us on Telegram, later I will help

2:05:34

answer your questions

2:05:36

about the short that we attended

2:05:38

tonight. Therefore, that's all

2:05:40

for tonight. I, Kartikair, take my leave

2:05:43

. Wasalamualaikum

2:05:45

warahmatullahi wabarakatuh. Yeah.

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