Lesson 02 � Data Science

DATA KI
LANGUAGE SEEKHO.

Statistics data ki language hai � mean, median, mode sab statistics ke concepts hain. Data Science without statistics possible nahi hai. Yeh lesson aapko basic statistical measures seekhayega jo har data analysis mein use hote hain.

WHY: Statistics kyun zaroori hai?

Data aata hai raw form mein � numbers, names, dates. Isme se meaning nikalne ke liye statistics chahiye. "Average marks kitne hain?" � mean se pata chalega. "Sabse zyada kisne score kiya?" � mode batayega. "Marks kitne spread hain?" � standard deviation dikhaayega. Statistics ke bina data sirf numbers ka hai, insights nahi.

HOW: mean kaise nikalein

Mean sabse basic statistical measure hai. Sab numbers ko add karo aur count se divide karo � bas. Python mein statistics.mean() se directly mil jaata hai.

import statistics

data = [85, 90, 78, 92, 88, 76, 95, 89]

# Mean - sab marks ka average
mean = statistics.mean(data)
print(f"Mean: {mean}") # 86.625

MEDIAN: middle value

Median tab useful hota hai jab data mein outliers hon � bahut chhote ya bahut bade values. Pehle data sort karo, phir middle value nikalo. Agar even count hai toh dono middle values ka average lo.

import statistics

data = [85, 90, 78, 92, 88, 76, 95, 89]

# Median - middle value
median = statistics.median(data)
print(f"Median: {median}") # 88.5

# Odd count mein seedha middle value
odd_data = [85, 90, 78, 92, 88]
print(f"Median: {statistics.median(odd_data)}") # 88

MODE: most frequent value

Mode sabse zyada baar aane wali value hai. Exam mein sabse common marks kaunse hain, ya shop mein sabse zyada bikne wali cheez kaunsi hai � mode se pata chalta hai.

import statistics

scores = [85, 90, 85, 78, 90, 85]

# Mode - sabse frequent value
mode = statistics.mode(scores)
print(f"Mode: {mode}") # 85

STD DEV & VARIANCE: data kitna spread hai

Standard deviation batata hai ki data mean se kitna door hai. Kam standard deviation ka matlab hai data tightly packed hai. Variance standard deviation ka square hai � spread ka squared measure.

import statistics

data = [85, 90, 78, 92, 88, 76, 95, 89]

# Standard Deviation - spread kitna hai
stdev = statistics.stdev(data)
print(f"Std Dev: {stdev:.2f}") # 6.30

# Variance - spread ka square
variance = statistics.variance(data)
print(f"Variance: {variance:.2f}") # 39.70

Try it: statistics calculate karo

Neeche ka editor Python jaisa hai. Yahan apna code likho aur "Run Python" dabao. Screen par results dikhenge � yeh browser-based simulation hai, real server nahi.

Sab statistics ek saath

Ek script mein saare basic statistics nikal sakte ho � comprehensive analysis hoti hai. Yeh approach real data projects mein bahut kaam aata hai.

import statistics

data = [85, 90, 78, 92, 88, 76, 95, 89]

# Comprehensive statistics
print(f"Count: {len(data)}")
print(f"Mean: {statistics.mean(data)}")
print(f"Median: {statistics.median(data)}")
print(f"Mode: {statistics.mode([85, 90, 85, 78, 90, 85])}")
print(f"Std Dev: {statistics.stdev(data):.2f}")
print(f"Variance: {statistics.variance(data):.2f}")
print(f"Min: {min(data)}")
print(f"Max: {max(data)}")
print(f"Range: {max(data) - min(data)}")

Common beginner mistakes

• Outliers se mean gerna: Agar data mein bahut extreme values hain (jaise 0 ya 1000), toh mean misleading ho sakta hai. Median zyada reliable hota hai.
• Mode bhoolna: Har data mein mode hota hai. Agar sab values alag hain toh mode meaningful nahi hoga � isliye pehle check karo ki koi repeated value hai ya nahi.
• Std dev aur variance confuse karna: Std dev original unit mein hota hai (marks), variance squared unit mein (marks�). Std dev zyada interpret hota hai.
• Sorted data nahi karna: Median nikalne ke liye data sort karna zaroori hai. Python ka statistics.median() automatically sort karta hai, lekin manually nikalte waqt yaad rakhna.