All language subtitles for 7 - I Didnt Learn Anything Try Again English

af Afrikaans
sq Albanian
am Amharic
ar Arabic
hy Armenian
az Azerbaijani
eu Basque
be Belarusian
bn Bengali
bs Bosnian
bg Bulgarian
ca Catalan
ceb Cebuano
ny Chichewa
zh-CN Chinese (Simplified)
zh-TW Chinese (Traditional)
co Corsican
hr Croatian
cs Czech
da Danish
nl Dutch
en English
eo Esperanto
et Estonian
tl Filipino
fi Finnish
fr French Download
fy Frisian
gl Galician
ka Georgian
de German
el Greek
gu Gujarati
ht Haitian Creole
ha Hausa
haw Hawaiian
iw Hebrew
hi Hindi
hmn Hmong
hu Hungarian
is Icelandic
ig Igbo
id Indonesian
ga Irish
it Italian
ja Japanese
jw Javanese
kn Kannada
kk Kazakh
km Khmer
ko Korean
ku Kurdish (Kurmanji)
ky Kyrgyz
lo Lao
la Latin
lv Latvian
lt Lithuanian
lb Luxembourgish
mk Macedonian
mg Malagasy
ms Malay
ml Malayalam
mt Maltese
mi Maori
mr Marathi
mn Mongolian
my Myanmar (Burmese)
ne Nepali
no Norwegian
ps Pashto
fa Persian
pl Polish
pt Portuguese
pa Punjabi
ro Romanian
ru Russian
sm Samoan
gd Scots Gaelic
sr Serbian
st Sesotho
sn Shona
sd Sindhi
si Sinhala
sk Slovak
sl Slovenian
so Somali
es Spanish
su Sundanese
sw Swahili
sv Swedish
tg Tajik
ta Tamil
te Telugu
th Thai
tr Turkish
uk Ukrainian
ur Urdu
uz Uzbek
vi Vietnamese
cy Welsh
xh Xhosa
yi Yiddish
yo Yoruba
zu Zulu
or Odia (Oriya)
rw Kinyarwanda
tk Turkmen
tt Tatar
ug Uyghur
Would you like to inspect the original subtitles? These are the user uploaded subtitles that are being translated: 1 00:00:00,750 --> 00:00:03,840 ‫Hold on, you didn't learn anything in the last video. 2 00:00:04,080 --> 00:00:05,650 ‫Oh, boy, you're a tough cookie. 3 00:00:05,820 --> 00:00:09,650 ‫Don't worry, we will get into more and more advanced topics later in the course. 4 00:00:09,660 --> 00:00:15,160 ‫But let's finish our talk on database's because again, I want to connect the dots for us. 5 00:00:15,660 --> 00:00:22,080 ‫We learned that a database is essentially a computer with some database software on top. 6 00:00:22,990 --> 00:00:28,930 ‫In a way, if you've ever used something like Google Sheets or Excel, that's a database, right? 7 00:00:28,990 --> 00:00:30,610 ‫It's a way for us to store data. 8 00:00:30,610 --> 00:00:34,360 ‫And the Excel software allows us to manipulate that data. 9 00:00:34,630 --> 00:00:38,660 ‫In a way, a pen and paper is a database as well. 10 00:00:38,680 --> 00:00:41,430 ‫It's not digital, but it's a way for us to store data. 11 00:00:42,710 --> 00:00:52,430 ‫Now, the reason we always see these images as database's is because of this, this is called drum memory 12 00:00:52,580 --> 00:00:55,130 ‫and I'll link to this resource so you can read more about it. 13 00:00:55,320 --> 00:01:01,420 ‫But back in the day before we had disk drives, this is how we stored data. 14 00:01:01,550 --> 00:01:03,440 ‫It was called drum memory. 15 00:01:03,440 --> 00:01:05,510 ‫And as you can see, it's cylindrical. 16 00:01:05,510 --> 00:01:12,740 ‫And that's why for historical reasons, just like our save button on a computer is a floppy disk, this 17 00:01:12,740 --> 00:01:16,040 ‫drum memory represents usually databases. 18 00:01:16,280 --> 00:01:19,870 ‫But again, in this day and age, they're all just computers. 19 00:01:20,780 --> 00:01:28,730 ‫So because a database is just a computer and some software, are there many different types of databases? 20 00:01:29,210 --> 00:01:30,440 ‫Well beyond there are. 21 00:01:30,680 --> 00:01:33,290 ‫I mean, we talked about Excel, right? 22 00:01:34,010 --> 00:01:39,450 ‫Essentially, you can use a spreadsheet that you get in Excel to store data. 23 00:01:40,250 --> 00:01:43,640 ‫So why don't we just use that for all companies? 24 00:01:43,640 --> 00:01:48,690 ‫Why do we need something bigger, something like a database that we're going to talk about? 25 00:01:49,160 --> 00:01:53,870 ‫You see, the problem with things like Excel is that eventually you'll get to a point where you have 26 00:01:54,080 --> 00:01:58,810 ‫too much data where an Excel or a spreadsheet just can handle it. 27 00:01:59,360 --> 00:02:04,610 ‫And on top of that, there are many things that databases that will learn about in this course will 28 00:02:04,610 --> 00:02:09,290 ‫solve, such as making sure that database has integrity. 29 00:02:09,320 --> 00:02:13,220 ‫So that is not everybody can just modify data or delete a database. 30 00:02:13,670 --> 00:02:17,780 ‫You can store terabytes of data, you can combine different databases. 31 00:02:17,780 --> 00:02:24,260 ‫You can automate steps and use programs to do some really interesting things that you might not be able 32 00:02:24,260 --> 00:02:25,580 ‫to do on a spreadsheet. 33 00:02:26,030 --> 00:02:28,490 ‫Again, something we'll cover later on in the course. 34 00:02:28,820 --> 00:02:32,740 ‫But some of the most popular databases are here. 35 00:02:33,020 --> 00:02:41,750 ‫This is just a fraction of the databases out there because data is so different, because every company, 36 00:02:41,750 --> 00:02:44,780 ‫every user uses data in a different way. 37 00:02:45,290 --> 00:02:52,100 ‫We have different databases that do things differently, and each one of these have pros and cons. 38 00:02:52,550 --> 00:02:57,200 ‫Now, the beauty is that in this course, we're going to cover some of the main databases and way to 39 00:02:57,200 --> 00:02:58,040 ‫interact with them. 40 00:02:58,040 --> 00:03:04,220 ‫And later on, we'll learn about the pros and cons so you can decide which database you should use based 41 00:03:04,220 --> 00:03:05,110 ‫on your situation. 42 00:03:05,630 --> 00:03:08,750 ‫But let's take Kako Corp as an example. 43 00:03:09,320 --> 00:03:13,370 ‫How would Kako Corp or a company use a database? 44 00:03:14,530 --> 00:03:20,740 ‫Well, we mentioned that there's lots of data coming into a company and a company as big as Kako Corp. 45 00:03:20,740 --> 00:03:22,640 ‫has a lot of uses for data. 46 00:03:23,500 --> 00:03:30,820 ‫For example, you might have product managers and product managers always have to know the product that 47 00:03:31,090 --> 00:03:31,960 ‫they are working on. 48 00:03:32,230 --> 00:03:37,240 ‫For example, drones, they need to get data and learn about the products. 49 00:03:37,240 --> 00:03:43,270 ‫Health, whether a product is working properly, learn from data, learn from different information 50 00:03:43,270 --> 00:03:44,830 ‫to perhaps improve a product. 51 00:03:45,870 --> 00:03:53,910 ‫You have things like marketers and marketers, you need to find out information from data you want to 52 00:03:53,910 --> 00:03:59,340 ‫analyze business decision, give you insights about how to market a product. 53 00:04:00,260 --> 00:04:08,170 ‫Or you might be a web developer, a mobile app developer that creates apps on phone or on the Web, 54 00:04:08,660 --> 00:04:15,710 ‫and those apps usually have things like user signings or profiles that you need to store somewhere so 55 00:04:15,710 --> 00:04:22,580 ‫that when a user comes back onto your app, they're able to reload their information or maybe they're 56 00:04:22,580 --> 00:04:27,590 ‫playing a game and they need to start the game from their last saved location. 57 00:04:28,960 --> 00:04:37,000 ‫You also have things like data analysts or data scientists that understand data, that analyze data 58 00:04:37,000 --> 00:04:44,710 ‫of a company and make decisions such as perhaps even building machine learning models so that a company 59 00:04:44,710 --> 00:04:51,790 ‫can serve better information to a user, better products to a user, analyze different parts of the 60 00:04:51,790 --> 00:04:58,240 ‫company to maybe see which employees should get a raise, who to hire. 61 00:04:58,970 --> 00:05:03,760 ‫And then you also have data engineers or database administrators. 62 00:05:04,060 --> 00:05:10,870 ‫These are the people that actually help set up the databases and a company update software, install 63 00:05:10,870 --> 00:05:14,260 ‫things, make sure that databases are connected with one another. 64 00:05:15,100 --> 00:05:21,490 ‫They might use things like Hadoop, Facebook's Presto, Google's big query, Amazon Redshift and set 65 00:05:21,490 --> 00:05:27,040 ‫up all this infrastructure in place for other parts of the companies to use databases. 66 00:05:28,280 --> 00:05:35,870 ‫Now, as you can see, there's lots of data and more importantly, there's lots of different databases, 67 00:05:35,870 --> 00:05:40,350 ‫different people in a company that use databases in different ways. 68 00:05:40,850 --> 00:05:41,720 ‫But hold on a second. 69 00:05:42,050 --> 00:05:45,460 ‫All this data is used differently by different people, right? 70 00:05:45,680 --> 00:05:48,470 ‫Different operations, different options. 71 00:05:48,800 --> 00:05:52,180 ‫Each of these people are interested in different parts of data. 72 00:05:52,670 --> 00:05:55,580 ‫And this is why you're taking the course. 73 00:05:56,770 --> 00:06:03,010 ‫You're taking this course because you want to hopefully be able to work in any of these fields, and 74 00:06:03,130 --> 00:06:09,610 ‫the good news is that even though there's all sorts of databases, even though there's all sorts of 75 00:06:09,610 --> 00:06:17,140 ‫jobs, one of the most common ways to work with databases to ask a database, a question what we call 76 00:06:17,140 --> 00:06:24,220 ‫a query, to interact with it, to use that information that is so useful, we use ask you, well, 77 00:06:24,580 --> 00:06:29,430 ‫a way for us to interact or communicate with that database. 78 00:06:29,440 --> 00:06:31,350 ‫And that's what this course is all about. 79 00:06:32,290 --> 00:06:34,360 ‫Let's take a break and I'll see you in the next video. 8770

Can't find what you're looking for?
Get subtitles in any language from opensubtitles.com, and translate them here.