🌟 A Desi Guide to Exploratory Data Analysis (EDA) in Urdu/Hindi🌟

Aslam-o-Alaikum futuristic data scientists! πŸŒπŸ’» Aaj hum desi tareeqay se EDA ke khoobsoorat duniya mein ghoomenge. Aaiye shuru karte hain! πŸŒŸπŸš€

Exploratory Data Anlaysis aik important task hy jis per saray AI depend karti hy.

1. Data Collection πŸ“š

Gathering Data:
Data collect karna jaise hum bazaar se sabziyan lete hain. Kuch data toh APIs aur CSV files se milta hai.
Desi Tarka: Jaise sabziyan taaza honi chahiye, waise hi data bhi fresh aur relevant hona chahiye!

Understanding the Data Source:
Data kahan se aya, yeh samajhna zaroori hai, jaise aap apni dadi se purani kahaniyan sunte hain.
Desi Tarka: Dadi ki kahaniyon mein hamesha ek seekh hoti hai, waise hi data source ko samajhne se insight milti hai!

2. Data Cleaning 🧹

Handling Missing Data:
Data mein kabhi kabhi kuch values missing hoti hain, unko sahi se handle karna important hota hai.
Desi Tarka: Jaise pakode mein namak kam ho jaye toh swaad nahi aata, waise hi missing data ko fill karna padta hai!

Removing Duplicates:
Duplicate values ko hatana padta hai, warna problem hoti hai.
Desi Tarka: Jaise duplicate samaan ko alag karna padta hai, waise hi duplicate data ko bhi!

Data Type Conversion:
Columns ko suitable data types mein convert karna padta hai.
Desi Tarka: Jaise aaloo ko katakar fry karte hain, waise hi data types ko convert karte hain!

3. Data Wrangling 🀹

Subsetting and Filtering:
Data ko chhan kar relevant parts ko select karna padta hai.
Desi Tarka: Jaise chaai mein se pattiyan chhan lete hain, waise hi data se unnecessary parts ko chhan lete hain!

Transforming Variables:
Maujooda variables ko transform karke naye variables banate hain.
Desi Tarka: Jaise aata goondh kar roti banate hain, waise hi variables ko transform karte hain!

Reshaping Data:
Data ko zaroorat ke mutaabiq reformat karna padta hai.
Desi Tarka: Jaise roti ko bel kar gol karte hain, waise hi data ko reshape karte hain!

4. Data Visualization 🎨

Univariate to Multivariate Visualization:
Ek se zyada variables ko plot karna, taake unke distributions aur relationships ko samajh sakein.
Desi Tarka: Jaise mithaiyon ki variety dekh kar choose karte hain, waise hi variables ko dekh kar insights nikalte hain!

5. Descriptive Statistics πŸ“Š

Central Tendency & Spread of Data:
Data ki central values aur spread ko samajhna padta hai.
Desi Tarka: Jaise biryani mein masala evenly spread hota hai, waise hi data spread ko check karte hain!

Correlation Analysis:
Variables ke beech ke relationships ko analyze karna padta hai.
Desi Tarka: Jaise chai aur biscuit ek saath achha lagta hai, waise hi correlations check karte hain!

6. Dimensionality Reduction πŸ”

Feature Engineering & Selection:
Maujooda features se naye features create karte hain aur relevant ones ko select karte hain.
Desi Tarka: Jaise masalon se khana tasty banta hai, waise hi features se data analysis mein maza aata hai!

7. Hypothesis Testing πŸ§ͺ

Formulating & Testing Hypothesis:
Clear aur testable hypothesis banate hain aur unko test karte hain.
Desi Tarka: Jaise pani puri mein paani test karte hain, waise hi hypothesis test karte hain!

8. Pattern and Anomaly Detection πŸ”Ž

Identifying Patterns:
Data mein patterns aur trends dhundhna.
Desi Tarka: Jaise mehendi lagate waqt patterns bana lete hain, waise hi data patterns find karte hain!

9. Modeling πŸ—οΈ

Building Models & Validation:
Problem ke hisaab se suitable models build karte hain.
Desi Tarka: Jaise khud ko sheeshe mein dekh kar tayyar hote hain, waise hi models ko validate karte hain!

10. Report and Documentation πŸ“

Documentation & Reporting:
Findings aur methodologies ko properly document karte hain.
Desi Tarka: Jaise kahaniyan likh kar yaad rakhne mein asani hoti hai, waise hi documentation helps in remembering the process!

πŸš€ Conclusion & Call to Action πŸš€

Ab jab aapko EDA ke steps aur desi tarkay pata chal gaye hain, toh kyun na aap bhi isko apne projects par implement karein? πŸŒŸπŸ” Explore, discuss aur implement karein aur duniya ko dikhayein apni data science ki skills!

πŸ’¬ Let’s Discuss! πŸ’¬

Aapke thoughts aur experiences humein comments section mein share karein! Mil kar data science ke duniya ko aur bhi mazedaar banayein! 🀝

πŸ”— Share & Connect! πŸ”—

Agar aapko yeh guide pasand aaya toh share zaroor karein, aur connect karein! Khush raho, aur data ke saath masti karte raho! πŸŒŸπŸ’»

33 Comments.

  1. Masha Allah bohot he achay tarikay se explain kia hai aap ne.The way you teach us these things is fantastic.its very easy to learn and understand

  2. It’s so helpful for me
    It’s easy to understand
    Your all materials is very helpful for me
    Thanks for providing this type of material Sir

  3. SALAM SIR! mujy aap k blog ka tariqa itna pasand aya k agar baqi blogs k likhne ka yahi tariqa hua tu INSHALLAH me baqi blogs ko b zaror parhonga.
    WONDERFULL SIR.

  4. AOA,
    A Desi Guide to Exploratory Data Analysis ko read kar k EDA k tamam step boht asani say samajh agy hay .
    ALLAH KAREEM, aap ko dono jahan ki bhalyean aata kary.AAMEEN

  5. jese ap ne hmare smjne k lie mushkil topics desi tarky wale bnae hy wese he Allah pak apki mushkilay asan kare amin

  6. In my life journey i saw alot youtube courses and in physical learn from alot of teachers but never see this type of teaching style . I really love this hard-working from my most respectfull Dr: Ammar i’ll put my 110% in data science journey. Allah Blessed you

  7. Mashallah Sir waqai ap nay apna name baba g bilqul bethreen rakha hain ,,,, Mushqil chezain bahot he asani say smajh aagai , aur Sir ap say ek guzarish hain k (in concept ko mazeed b explian karain sir minimum 5 example k sath

  8. Apki content writing bhut hi umda hai or samjnay mai bhut asan hai or example bhut mazaya han. Big salute to you ustad-e-mohotram (Sir Ammar) or aaj hai bhi “Happy Teacher Day”

  9. I wanted to drop you a quick note to say that I’m a big fan of your blog. Your content is fantastic, and I find it both enjoyable and informative. Keep up the great work, as I look forward to reading more from you in the future!

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