Lecture 09: Data Arrangement for Data Science

Nafees AI Lab

Data arrangement, also known as data organization, is the process of structuring and organizing data in a way that is easily understood, manageable, and useful for analysis. It involves arranging data in a specific order or format, such as by creating tables, lists, or hierarchies, to make it easier to locate, compare, and analyze.

Transcript

00:00Hello guys and gullies, welcome back.

00:30Normally, you have two types.

00:37Two types.

00:41Two types.

00:42Pahli type is structured data.

00:47Unstructured data.

00:57Now, two types.

00:59You can see CSV file,

01:02which is comma separated file,

01:04or Excel sheet.

01:05What do you see in Excel sheet?

01:07For example,

01:09what do you see?

01:11What do you see?

01:13These are comma separated values.

01:15These are structured data.

01:19Normal example.

01:21Structured data.

01:22You have to go to hospital,

01:24which is the patient's record.

01:26It's structured data.

01:29The name is weight, blood pressure, and so on.

01:34This is structured data.

01:36This is structured data.

01:38This is basically

01:39you have to column and rows

01:41in the pattern.

01:42This is the best example

01:44that you listen to the audio.

01:46This is structured data.

01:48This is the reason

01:49that an audio is different.

01:51This is a different image.

01:53You can see it.

01:54Image is one structured data.

01:55Okay.

01:57I will try to say

01:58image

01:59and audio

02:01and audio

02:03what are unstructured data?

02:05Now,

02:07structured data

02:08and unstructured data

02:09I have to tell you

02:10that CSV

02:11is comma separated files.

02:12Now,

02:13structured data

02:14and unstructured data

02:15are two types.

02:17One type is static data and one type is time series data.

02:28Static data is the best example.

02:31You can see a comma separated file.

02:34CSV file.

02:36Time series data is the best example.

02:39Stock market.

02:41Stock market highest point and lowest point.

02:45It is basically a time.

02:48Time series data is the best example.

02:53Basically, you have a problem with static data.

02:59Time series data is the best example.

03:02Workflow is the best example.

03:05It is very important.

03:07Normally, you have a CSV file.

03:11This data you can open using a Jupyter Notebook.

03:17Jupyter Notebook, we will see what is.

03:21This is an IDE.

03:23This is an IDE.

03:25It is an IDE.

03:27For example, this data we can see.

03:29For example, this data we want to see.

03:31For example, this data we have found.

03:33You have seen.

03:34You have seen.

03:35You have seen.

03:36You have seen your software it was open.

03:38Right?

03:39It's a software, it's an Excel.

03:40You have seen your software.

03:42It's an Excel itself.

03:43You can open it in Excel.

03:45However, normally,

03:47when you use the machine learning project,

03:49you can use Jupyter Notebook.

03:51Jupyter Notebook

03:53You can use it in a good way.

03:55No need to worry at all.

03:57No need to worry at all.

03:59You can visualize it.

04:01You can visualize it.

04:03V-I-S-U-A-L-I-Z-E

04:05Visualize.

04:07Now, we can explore it

04:09for a library.

04:11This library is called Pandas.

04:15The Pandas library is basically

04:19data to explore, plot

04:21and plot.

04:23You can use it.

04:25You can use it.

04:27You can use it.

04:29You can use it.

04:31You can use it.

04:33You can use it.

04:35You can use it.

04:37Mat, plot, lib, mat, plot,

04:39library.

04:41Mat, lab.

04:43Plotting function

04:45Plot, plot, visualize

04:47data.

04:49You can all look at it.

04:51Look at it.

04:52And there's no data.

04:53You can use it.

04:55What's the problem?

04:57It's all.

04:59Finally,

05:01you can use it.

05:03اس کے اوپر ڈپلائے کرتے ہیں

05:04using scikit learn

05:05جو کہ again

05:06ایک machine learning library ہے

05:07python میں لکھی گئی ہے

05:09تو اس طریقے سے

05:10ایک پورا workflow

05:12آپ کے پاس ہوتا ہے

05:13کہ ایک data آتا ہے

05:14آپ اس کو

05:14jupiter notebook میں open کرتے ہیں

05:16اور پھر

05:17اب data کو visualize کرتے ہیں

05:20and then you apply

05:21a machine learning model

05:22اب یہ ساری جو

05:24یہ جو jupiter notebook ہے

05:25یہ جو visualization کے لیے

05:27یہ سارے tools ہیں

05:28اور ان tools کو بھی

05:29ہم نے تفصیل کے ساتھ سیکھنا ہے

05:30at the moment صرف

05:31آپ کو ایک workflow بتانا ہے

05:32کہ کس طریقے سے

05:33ایک workflow

05:34ایک complete project

05:35کا جو ہے وہ

05:36process چلتا ہے

05:37تو یہاں پہ

05:38کر دیتے ہیں

05:38is lecture

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