Konfigurasi Jump Host Dengan .htaccess dalam Apache Webserver

Bila webserver dah ready, ini jangan dilupakan iaitu Fail `.htaccess` adalah fail konfigurasi yang digunakan dalam pelayan web Apache untuk mengkonfigurasi paparan direktori. Ia membolehkan pengguna mengawal konfigurasi tertentu untuk direktori secara khusus tanpa perlu mengubah konfigurasi global pelayan web. 

Fail `.htaccess` biasanya digunakan untuk menyekat akses ke fail-fail dalam direktori, menyembunyikan fail-fail tertentu, menghantar permintaan ke halaman lain, dan banyak lagi. Ini membolehkan pengurusan konfigurasi yang lebih terperinci dan fleksibel bagi laman web yang dihoskan pada pelayan web Apache.

Ini bagi nak konfigurasi .htaccess agar hanya  boleh masuk dari secure jump host shj.

Keselamatan Siber - Comei Red Team Aku

Dalam menguji keteguhan pertahanan siber memerlukan red team. Red team dalam pasukan cybersecurity akan melakukan serangani terhadap sistem, jaringan, atau aplikasi sesebuah organisasi untuk kenalpasti kelemahan pertahanan dan melatih tindak balas (blue team) dalam menangani serangan siber.

Red team bertindak seperti penyerang atau sebenarnya untuk menguji keefektifan sistem pertahanan dan mendapat gambaran tentang bagaimana penyerang atau hacker dapat memanfaatkan vulnerability/titik kelemahan yang ada.

Lupa katalaluan Section OneNote?

Anda mungkin berada dalam situasi di mana anda meletakkan kata laluan pada seksyen di OneNote, jika dilindungi kata laluan dan anda tidak menggunakannya untuk tempoh yang lama, anda mungkin akan cuba mengaksesnya dan tidak ingat kata laluan tersebut. buat masa ini tiada penyelesaian sehingga kini.

Kalau masih ada gambaran tentang password tersebut boleh la cuba panduan ini ... panduan ini untuk dapatkan semula akses. Mungkin password yang digunakan sayang,Password , 123456789 dsbnya

Bagi mereka yang guna onenote plak bila dah lama lupa la password, tetapi ... ini adalah satu contoh bagaimana kombinasi password generator boleh digunakan untuk meneka password yang kita lupa sebagai source untuk dicuba.

Jom mula

First kena la download dan install Password Generator ... boleh download Password Generator di

1.Latest - Terkini

2.Old 

link kat atas tu ... dah tu buatkan satu file yang ada password2 yang kita mungkin ingat2 lupa tu

  

Step dia

Pilih fail frasa perkataan yang nak dicipta.

Hilangkan tanda centang pada "sertakan character"

Hilangkan tanda centang pada "sertakan words"

Tanda centang pada "format kata laluan" dan masukkan: {1}W{2-3d}

Ini akan merangkumi sebuah perkataan dari fail frasa perkataan dan kemudian 2 hingga 3 digit.

W = perkataan dari fail frasa perkataan

 d = digit 0-9

tapi ada mcm2 format lagi ... tu kena bacalah yer.

Next:

Klik uji dengan mengklik "Generate," klik butang ke bawah pilih Generate to File dan tetapkan di mana fail senarai kata laluan dicipta.

Cth pilih c:\password tech\trypass.txt

Masukkan bilangan kata laluan yang ingin dihasilkan.

Perhatikan bahawa terdapat 90 kombinasi 2 digit tanpa pengulangan nombor 0-9 dan 720 kombinasi 3 digit.

Jadi:

sayangPassword{2 digit} - 90, katakanlah 100

Passwordsayang{2 digit} - 90, katakanlah 100

sayangPassword{3 digit} - 720, katakanlah 1000

Passwordsayang{3 digit} - 720, katakanlah 1000

Terdapat hasilnya password dengan 2,200 kombinasi.

Lebih baik kita buat 5000 password dan kita tekan terus "Generate

So kita telah hasilkan 5000 password dalam file c:\password tech\trypass.txt

jom kita try Brute Force Password Onenote 

kena hasilkan scripts untuk dijalankan diwindows kita

1. Install AutoHotKey

Download dan install AutoHotkey dari official website: AutoHotkey Download

2. Cipta fail ujian bernama bruteforce.ahk:

Buka penyunting teks dan cipta fail bernama bruteforce.ahk 

Salin dan tampal skrip berikut ke dalam fail:

-----------------Start Copy Selepas Ini --------------------

DetectHiddenWindows on

DetectHiddenText on

#k::

SetKeyDelay 30

Loop, read, C:\IT\appleorange-combos.txt

{

  TrayTip Now trying:, %A_LoopReadLine%, 1 ;Creates tooltip so we can monitor the progress through wordlist.

  SendRaw %A_LoopReadLine% ;Type the current line into box

  Send {enter} ;Submit this password

  Sleep 300 ;Wait while the password is tried

  WinGetActiveTitle, varRespondingWindow ;Check resulting dialogue (look for 'invalid passphrase' error)

  

  if ( varRespondingWindow != "Protected Section" ) {

    ;DEBUG - We found the password. (If the window DOESN'T contain the words Protected Section then we cracked it).

MsgBox % "Password Found: " . A_LoopReadLine

return

  } else {

    ;DEBUG - It was wrong 

  }

}

return

#q::

;This is to exit the script

Exit

return

----------------- Tamat  --------------------

Jom kita try Brute Force Password Onenote 

kena hasilkan scripts untuk dijalankan diwindows kita 

1. Buka Onenote
2. Cuba buka bahagian yang dilindungi
3. Sampai ke prompt kata laluan
4. Buka skrip bruteforce.ahk (anda akan lihat ikonnya di systray) Tekan Winkey + k

Skrip akan melakukan tugasnya dan mula menguji kata laluan dari senarai kata laluan yang telah dibuat sebelumnya.

Untuk keluar dari skrip, tekan Win + q 

Panduan ini hanya akan berjaya jika anda mempunyai idea kata laluan yang anda gunakan di OneNote. Ia tidak akan meretas kata laluan rawak.

AI Pengenalan

Tentang Asas Kecerdasan Buatan (AI):

Asas Kecerdasan Buatan (AI) merujuk kepada bidang dalam sains komputer yang berusaha untuk mencipta sistem yang dapat melakukan tugas-tugas yang biasanya memerlukan kecerdasan manusia. AI menggunakan pelbagai teknik dan pendekatan untuk menyediakan komputer dengan keupayaan untuk mengenali pola, mengadakan penalaran, membuat keputusan, dan menyelesaikan masalah dengan cara yang serupa dengan manusia.

Asas-asas AI merangkumi:

1. Pembelajaran Mesin (Machine Learning): Ia adalah satu kaedah yang menggunakan data dan algoritma untuk mengajar komputer mengenali pola dan membuat ramalan atau keputusan tanpa perlu secara eksplisit diprogramkan. Terdapat beberapa jenis pembelajaran mesin, termasuk pembelajaran bersepadu, pengawasan, dan tak pengawasan.

2. Penalaran Berdasarkan Aturan (Rule-based Reasoning): Ia melibatkan penggunaan aturan logik dan penalaran formal untuk membolehkan komputer membuat kesimpulan berdasarkan maklumat yang diberikan.

3. Penglihatan Komputer (Computer Vision): Ia melibatkan penganalisisan dan pengenalpastian imej dan video oleh komputer. Dengan menggunakan algoritma penglihatan komputer, sistem dapat mengesan objek, mengenali muka, membaca teks, dan melakukan tugas lain yang berkaitan dengan persepsi visual.

4. Pemprosesan Bahasa Semula (Natural Language Processing): Ia melibatkan pemahaman dan penghasilan bahasa manusia oleh komputer. Sistem AI dapat membaca, menafsirkan, dan memberi respon kepada teks atau ucapan manusia melalui teknik seperti penguraian sintaksis, analisis sentimen, dan pemodelan bahasa.

5. Sistem Pakar (Expert Systems): Ia melibatkan penggunaan pengetahuan dan aturan berstruktur untuk memecahkan masalah yang kompleks dalam bidang tertentu. Sistem pakar dapat meniru pemikiran manusia dan memberikan nasihat atau penyelesaian berdasarkan pengetahuan yang dimasukkan.

ChatGPT:

ChatGPT adalah model bahasa yang dibangunkan oleh OpenAI. Ia adalah contoh daripada satu-satunya jenis AI, iaitu model pembelajaran bersepadu yang menggunakan Transformasi Gegelung Bertingkat (GPT). ChatGPT dilatih dengan menggunakan jutaan perkataan teks yang dikumpulkan dari sumber-sumber di Internet.

Tujuan ChatGPT adalah untuk menyediakan respon yang relevan dan berarti kepada pertanyaan atau permintaan input pengguna. Ia menggunakan pelbagai teknik AI, termasuk pemprosesan bahasa semula, pemodelan bahasa bersepadu, dan pembelajaran mesin. ChatGPT telah didorong oleh kecerdasan tiruan untuk menghasilkan output yang sesuai dan memberikan respons seperti yang diharapkan oleh pengguna.

Walaupun ChatGPT dapat memberikan jawapan yang berinformasi dan mengikut konteks, perlu diingat bahawa ia adalah model bahasa yang dibina berdasarkan teks yang ada pada masa lalu. Oleh itu, ia mungkin tidak mempunyai pengetahuan terkini atau maklumat tentang peristiwa yang berlaku selepas tarikh pemotongan pengetahuan model, dan mungkin tidak dapat memberikan jawapan yang tepat dalam konteks masa kini.

Microsoft Excel: Bagaimana nak convert tarikh ke US format

 Aduhai, hatri ini mmemang la tergesa-gesa nak mencari dan mengisi maklumat dalam pengurusan kewangan. sepatutnya mudah jer dari bank statement yang kjita download tu terus jer upload ntah camne ari ni x leh plak. 

Boring tul la, ingat kejap jer siap tapi ntah dah jadi lambat. Ok baik ... so bermula misi mencari apa masalah dnegan pakcik google. AKhir berjaya juga dapat formula ni. cuma leceh sket terpaksa buat beberapa kerja tambahan. 

Ini ngupa file asal statement yang kita download dari bank, rupanya format yang dia bagi dalam format Malaysia manakala format yang dikehendaki oleh Wallet by BudgetBaker plak dalam bentuk US format. Tu susah juga aku nak cari contoh x jumpa plak dalam portal https://support.budgetbakers.com/hc/en-us .

So aku pon cari la formula untuk conver kepada US format date ni.  So caranya ialah 

Sediakan satu cell kosong ( aku letak selepas date yg bank bagi) masukan formula formula =TEXT(A11, "mm/dd/yyyy"),  Dalam kes aku cell B11, nengok  screenshot: tu. press Enter dan select cell B11, drag the fill handle ke range you korang nak ... tada dapat ate dalam US tu. Ok ... tak

NMAP untuk port scanning ... Meh cuba bhai

run nmap -sC -sV untuk dapatk detailed result

dah dapat open port tu cuba check pc or server tu kegunaan dia utk apa

kalau perlu open port tu so takde masalah la..tp kalau tak perlu better off dan disable server dan port2 tu

kalau -sC -sV 

-sC - run nmap scripts

-sV - dia akan check version setiap apps tu

so kalau dah dapat detailed version tu

kalau kita nak penetrate kita boleh tengok kat cve db tu atau metasploit

ada google hacking db kan ada cve db..so kita boleh tengok apps kita ni vulnerable ke tak

kalau ada vulnerable ..kita kena le upgrade atau update

sebelum upgrade or update tu kena tengok le fungsi apps akan terganggu ke tak kalau kita upgrade

so ada byk la yg kena tambah baik kalau pelru upgrade..contoh kalau run php..kalau nak upgrade ke versi php7

mungkin kena buat restruktur balik code php tu sebab du2 version php5 dan php7 ni dah lain

application or server ni running untuk public access ke atau just dalaman saja

nampak mcm server microsoft ni kan ada microsoft service dalam result ni

kalau dapat versi server dan semak pada cve tu

https://www.exploit-db.com/

cari kat sini le..kalau ada dalam list..kena upgrade le apps or server tu

kalau tak mmg tunggu masa je le org nak cuba tu nanti

Data Analysis Iteration

WEBVTT

1
00:00:01.440 --> 00:00:05.063
This lecture's gonna provide an overview
of the cycle of data analysis.

2
00:00:10.191 --> 00:00:14.060
Data analysis is a complex process
that can involve many pieces and

3
00:00:14.060 --> 00:00:15.790
many different tools.

4
00:00:15.790 --> 00:00:18.480
But fundamentally,
there are only three parts to it.

5
00:00:19.540 --> 00:00:22.180
The first part is setting expectations.

6
00:00:22.180 --> 00:00:24.460
The second part involves
collecting information and

7
00:00:24.460 --> 00:00:26.990
comparing your expectations to data, and

8
00:00:26.990 --> 00:00:32.220
the last part Involves reacting to data,
and revising your expectations.

9
00:00:32.220 --> 00:00:33.750
So that's basically it.

10
00:00:33.750 --> 00:00:37.990
Those are the three parts of data analysis
that you often will cycle through,

11
00:00:37.990 --> 00:00:41.290
many times, in the course of
analyzing any given data set.

12
00:00:41.290 --> 00:00:42.820
So I'm gonna break down each one of these

13
00:00:43.870 --> 00:00:47.190
pieces to give you a little bit of
a description of what they are, and I'll

14
00:00:47.190 --> 00:00:51.690
give you a little example of kinda how
they might be applied in the real world.

15
00:00:51.690 --> 00:00:55.913
So steady expectations involve
deliberately thinking about

16
00:00:55.913 --> 00:00:58.599
what you're gonna do before you do it.

17
00:00:58.599 --> 00:01:02.585
Now, the idea in any part of data analysis
is that everything you do is gonna

18
00:01:02.585 --> 00:01:06.511
have some sort of consequence,
whether it's collecting data, whether

19
00:01:06.511 --> 00:01:11.035
it's fitting a model, whether it's asking
a question or making some sort of plot.

20
00:01:11.035 --> 00:01:15.494
Everything you do there will be some sort
of action, and the point is you wanna

21
00:01:15.494 --> 00:01:19.490
think about what that consequence
is gonna be, before you do it.

22
00:01:19.490 --> 00:01:22.850
And that way you set the expectations for
yourself, and

23
00:01:22.850 --> 00:01:27.980
you can determine whether the reality
kind of meets that expectation.

24
00:01:29.635 --> 00:01:33.165
So that's the first part
of the data analysis cycle.

25
00:01:33.165 --> 00:01:37.685
Once you've set your expectations the next
thing you wanna do is collect some data or

26
00:01:37.685 --> 00:01:42.380
collect some information that will
allow you to compare those expectations

27
00:01:42.380 --> 00:01:43.775
to reality.

28
00:01:43.775 --> 00:01:48.170
And so,
collecting that information is key,

29
00:01:48.170 --> 00:01:51.030
because it will tell you whether or
not your expectations were right.

30
00:01:51.030 --> 00:01:51.930
Whether they were wrong.

31
00:01:51.930 --> 00:01:53.713
Whether they were too high, too low,

32
00:01:53.713 --> 00:01:56.568
whatever it is depending on
the problem you're working on.

33
00:01:56.568 --> 00:02:01.009
And then, once you've collected that
information, and compared it to your

34
00:02:01.009 --> 00:02:05.541
expectations you can react to it, and
maybe change your behavior in some way.

35
00:02:05.541 --> 00:02:09.702
So the last part of the data analysis
cycle is to think about what have

36
00:02:09.702 --> 00:02:14.309
we learned from the data, from our
expectations, and their comparison.

37
00:02:14.309 --> 00:02:16.350
What would we do differently next time.

38
00:02:17.400 --> 00:02:20.870
Did we match our expectations,
did they not match, why or why not.

39
00:02:20.870 --> 00:02:22.443
So that's the third part.

40
00:02:22.443 --> 00:02:27.341
And then, once you've completed the third
part and you've revised your expectations,

41
00:02:27.341 --> 00:02:31.401
you may go back, with these revised
expectations and collect more data and

42
00:02:31.401 --> 00:02:35.267
try to match them again, and
then this iteration continues, often for

43
00:02:35.267 --> 00:02:37.990
many different times in
any given data analysis.

44
00:02:40.470 --> 00:02:44.830
So I just wanna give you a quick
example of how you can use these

45
00:02:44.830 --> 00:02:50.190
three components in a kind of generic or
kind of commonplace setting.

46
00:02:50.190 --> 00:02:53.360
So the basic example I'm gonna present
here is going out to dinner with

47
00:02:53.360 --> 00:02:54.510
your friends.

48
00:02:54.510 --> 00:02:56.490
So suppose you're going out to dinner and

49
00:02:56.490 --> 00:02:59.140
the restaurant you're going
to is a cash only place.

50
00:02:59.140 --> 00:03:02.410
So the question you have to ask yourself
is how much money should you bring.

51
00:03:04.005 --> 00:03:09.235
And the basic activity you're gonna
engage in is eating a meal, and you're

52
00:03:09.235 --> 00:03:13.520
gonna check for the bill, and you're gonna
have to pay, money to pay for the meal.

53
00:03:13.520 --> 00:03:17.869
But before you do that, you gotta figure
out how much money to bring, and so

54
00:03:17.869 --> 00:03:22.585
you have to figure out well, what's your
expectation for the cost of this meal.

55
00:03:22.585 --> 00:03:25.299
Maybe you've dined at this
restaurant all the time, so

56
00:03:25.299 --> 00:03:27.448
you know exactly how much it's gonna cost.

57
00:03:27.448 --> 00:03:32.487
Maybe you know, well in this city, the
typical meal costs this many dollars, and

58
00:03:32.487 --> 00:03:37.403
so I'll just bring that much money,
cuz this is an average kind of restaurant.

59
00:03:37.403 --> 00:03:38.260
Maybe you know,

60
00:03:38.260 --> 00:03:42.065
well the most expensive restaurant in
this city costs this many dollars.

61
00:03:42.065 --> 00:03:44.936
So I know it's not gonna
cost this more than that, so

62
00:03:44.936 --> 00:03:49.180
I'll just bring that to kind of serve as
an upper bound on how much money I might

63
00:03:49.180 --> 00:03:51.223
end up spending at this restaurant.

64
00:03:51.223 --> 00:03:53.365
You might ask your friends,
if they've been their before,

65
00:03:53.365 --> 00:03:54.650
how much does this place cost.

66
00:03:54.650 --> 00:03:56.060
Or you might Google the restaurant and

67
00:03:56.060 --> 00:03:59.430
maybe look up the menu to see what
the meal typically costs there.

68
00:03:59.430 --> 00:04:04.388
At any rate, before you've gone to the
restaurant and eat the meal, you can use

69
00:04:04.388 --> 00:04:08.310
any sort of opreory information
to set up your expectations for

70
00:04:08.310 --> 00:04:10.684
what the cost is ultimately gonna be.

71
00:04:10.684 --> 00:04:13.220
Before you observe the real thing.

72
00:04:14.430 --> 00:04:19.176
So once you've set your expectations, you
can figure out how much money to bring.

73
00:04:19.176 --> 00:04:22.339
The actual collecting of the data
involves going to the restaurant and

74
00:04:22.339 --> 00:04:23.255
getting the check.

75
00:04:23.255 --> 00:04:25.839
So once you've gotten the check,

76
00:04:25.839 --> 00:04:29.500
you observed the reality
of what the meal costs.

77
00:04:30.640 --> 00:04:32.560
And there's two possibilities.

78
00:04:32.560 --> 00:04:35.250
One is that,
that cost meets your expectation.

79
00:04:35.250 --> 00:04:38.510
So suppose you thought
it was gonna be $30, and

80
00:04:38.510 --> 00:04:40.710
it ended up being $30, then that's great.

81
00:04:40.710 --> 00:04:41.630
You know exactly,

82
00:04:41.630 --> 00:04:45.810
you brought the right amount of money,
and then you can pay for the meal.

83
00:04:45.810 --> 00:04:49.696
The other possibility is that
the expectations don't match of what

84
00:04:49.696 --> 00:04:50.657
the reality is.

85
00:04:50.657 --> 00:04:55.386
So thought it was $30 and
it ended up being $40.

86
00:04:55.386 --> 00:04:59.120
And so, you have to ask yourself
then why do you have that mismatch.

87
00:04:59.120 --> 00:05:04.239
Why is it that you thought it was $30 and
the meal turned out to be $40.

88
00:05:04.239 --> 00:05:05.630
So there's two possibilities.

89
00:05:05.630 --> 00:05:08.000
One is that your expectations were wrong.

90
00:05:08.000 --> 00:05:11.820
So you thought that the restaurant
was cheaper than it actually was.

91
00:05:11.820 --> 00:05:15.393
Another possibility is that there's
something wrong with the data, for

92
00:05:15.393 --> 00:05:15.920
example.

93
00:05:15.920 --> 00:05:19.192
It's possible that they added up the check
wrong, maybe they charged you for

94
00:05:19.192 --> 00:05:21.169
something for
that you didn't actually eat.

95
00:05:21.169 --> 00:05:24.835
So you can look at the check to see if
there is a problem with the data that you

96
00:05:24.835 --> 00:05:25.500
collected.

97
00:05:29.040 --> 00:05:34.040
One thing to note about
this example is that it was

98
00:05:34.040 --> 00:05:39.450
easy to know whether your expectations
were matched with the data or not.

99
00:05:39.450 --> 00:05:43.892
So for example, if your expectation was
the meal would cost $30, and then it

100
00:05:43.892 --> 00:05:48.691
actually cost $40, you know immediately
that your expectations were not right.

101
00:05:48.691 --> 00:05:52.200
The meal was $10 more than you
actually thought it was gonna be.

102
00:05:52.200 --> 00:05:55.249
And so,
you can make that conclusion very quickly.

103
00:05:55.249 --> 00:05:57.298
Another possibility, for example,

104
00:05:57.298 --> 00:06:01.405
is that you could've said well
the meal being between 0 and $1,000.

105
00:06:01.405 --> 00:06:05.885
And so, when the data actually comes
in and you see the check is $40 then it

106
00:06:05.885 --> 00:06:10.930
actually matches your expectation which
is that it's between 0 and $1,000.

107
00:06:10.930 --> 00:06:15.830
But because your original
expectation was so diffused, and

108
00:06:15.830 --> 00:06:20.535
so kind of general,
you don't really learn that much from

109
00:06:20.535 --> 00:06:25.650
collecting the data given your
very diffused expectation.

110
00:06:25.650 --> 00:06:30.160
So this brings us to an important point
which is that it's important to have

111
00:06:30.160 --> 00:06:32.370
a very sharp expectation or

112
00:06:32.370 --> 00:06:36.180
a sharp hypothesis about what
you're trying to investigate.

113
00:06:36.180 --> 00:06:38.940
When I said that I expected
the meal to be $30,

114
00:06:38.940 --> 00:06:44.210
it was very easy to know when
my expectations were not met.

115
00:06:44.210 --> 00:06:48.575
But if my expectation was very diffused
and not sharp at all, like between 0 and

116
00:06:48.575 --> 00:06:52.880
1,000, then, collecting the data
doesn't really help you.

117
00:06:52.880 --> 00:06:57.430
Or it doesn't help you learn the process
you're trying to study or in this case,

118
00:06:57.430 --> 00:06:59.360
the cost of the meal at this place.

119
00:06:59.360 --> 00:07:02.600
So ultimately,
what we're leaning toward with

120
00:07:02.600 --> 00:07:06.820
setting your expectations in collecting
data is called a change in behavior or

121
00:07:06.820 --> 00:07:09.620
an understanding of the mechanism
you're trying to study.

122
00:07:09.620 --> 00:07:13.370
What did we learn, and
what would you do differently next time?

123
00:07:13.370 --> 00:07:16.465
So in this scenario where you
thought it was gonna be $30 and

124
00:07:16.465 --> 00:07:20.589
it ended up being $40, well then the next
time you might bring an extra $10.

125
00:07:20.589 --> 00:07:23.211
If you originally thought it
was gonna be between 0 and

126
00:07:23.211 --> 00:07:27.429
$1,000 then the cost ended up being $40,
it's not clear that you would change

127
00:07:27.429 --> 00:07:29.992
anything about your behaviour
based on this data.

128
00:07:29.992 --> 00:07:35.490
And so, if there is no change
in what you might think or

129
00:07:35.490 --> 00:07:38.950
what you might do based on
the collection of the data and

130
00:07:38.950 --> 00:07:43.140
matching it with your expectations,
then that's often a sign that

131
00:07:43.140 --> 00:07:46.740
either the evidence from your experiment
is not very strong or the data analysis

132
00:07:46.740 --> 00:07:51.420
was not able to generate enough evidence,
or there may be some other problem.

133
00:07:51.420 --> 00:07:55.890
With your study or
your data analysis process.

134
00:07:55.890 --> 00:08:00.130
So setting the right expectations and
making them as sharp as possible

135
00:08:00.130 --> 00:08:03.730
is a really key element to this
whole data analysis cycle.