Data Science & Machine Learning

🚀 Data Science Roadmap 2026 📘 Phase 2: Mathematics for Data Science 📖 Topic 7: Descriptive Statistics — Range, Percentiles, Quartiles, IQR & Five-Number Summary Welcome back! 👋 In the previous lesson you covered Probability Distributions. Now we’re moving to Descriptive Statistics — how we summarize data without predicting the population. Today we’ll cover: Range, Percentiles, Quartiles, IQR, Five-number summary, Outlier detection These are core for EDA. 🔹 1. What is Descriptive Statistics? Summarizes key characteristics of a dataset. Example: Salaries: 30000, 35000, 40000, 45000, 50000 Instead of checking each value, use: Min, Max, Mean, Median, Quartiles, Percentiles, Std Dev 🔹 2. Range Formula: Range = Maximum − Minimum Example: 10, 20, 30, 40, 50 → Range = 50 − 10 = 40 Note: Very sensitive to outliers. 50 → 500 makes range jump to 490. 🔹 3. Percentiles ⭐ Value below which X% of observations fall. 50th Percentile = Median 25th Percentile = 25% at or below 90th Percentile = 90% at or below 🔹 4. Real-World Example 90th percentile score ≠ 90% marks. It means you did better than ∼90% of people. 🔹 5. Quartiles Divide data into 4 equal parts: Q1 = 25th percentile Q2 = 50th percentile = Median Q3 = 75th percentile 🔹 6. Visualizing Quartiles 0% ---- Q1 ---- Q2 ---- Q3 ---- 100% 25% 50% 75% 🔹 7. Interquartile Range (IQR) ⭐ Formula: IQR = Q3 − Q1 Example: Q1=20, Q3=60 → IQR = 40. Middle 50% spans 40 units. 🔹 8. Why IQR Matters Less affected by outliers than Range. Data: 10,20,30,40,50,1000 → Range=990 but IQR ignores the 1000. 🔹 9. Detecting Outliers Using IQR ⭐ Lower Bound = Q1 − 1.5 × IQR Upper Bound = Q3 + 1.5 × IQR Values outside = potential outliers 🔹 10. Outlier Example Q1=20, Q3=60 → IQR=40 Lower = 20-60 = -40 Upper = 60+60 = 120 So < -40 or > 120 are outliers 🔹 11. Five-Number Summary ⭐ 1. Minimum 2. Q1 3. Median 4. Q3 5. Maximum Ex: 10, 20, 30, 40, 50 🔹 12. Box Plot Visualizes the 5-number summary. Box = Q1 to Q3. Line inside = Median. Whiskers = range without outliers. 🔹 13. Python Example

import numpy as np

data = [10, 20, 30, 40, 50, 60, 70]
q1 = np.percentile(data, 25)
median = np.percentile(data, 50)
q3 = np.percentile(data, 75)
iqr = q3 - q1
print("Q1:", q1, "Median:", median, "Q3:", q3, "IQR:", iqr)

🔹 14. Descriptive Statistics in Pandas

import pandas as pd

df = pd.DataFrame({"Salary": [30000, 35000, 40000, 45000, 50000]})
print(df["Salary"].describe())

describe() gives Count, Mean, Std, Min, 25%, 50%, 75%, Max 🔹 15. Real-World Example Transactions: Q1=₹500, Median=₹1000, Q3=₹2000 → IQR=₹1500 Use IQR to flag fraud, bulk orders, errors, or VIP customers. Investigate before deleting. 🔹 16. Range vs IQR Range: Easy but outlier-sensitive IQR: Middle 50% only, robust to outliers 🔹 17. Percentile vs Percentage Percentage = out of 100. Ex: 80% marks Percentile = relative position. Ex: 90th percentile 🔹 18. Common Mistakes ❌ 90th percentile = 90% score ❌ Deleting all outliers blindly ❌ Thinking IQR covers all data 🎯 Practice Questions 1. Range of 10, 20, 30, 40, 50 = ? 2. Median = which percentile? 3. Q1=25, Q3=75 → IQR = ? 4. Upper outlier boundary formula? 5. 5 components of five-number summary? 🎯 Key Takeaways ✅ Range = Max - Min ✅ Q1=25th, Q2=50th=Median, Q3=75th ✅ IQR = Q3 - Q1 ✅ 5-number summary = Min, Q1, Median, Q3, Max ✅ Percentile ≠ Percentage 👉 Double Tap ❤️ For More ----- 2.46 ₽ · /balance_help

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✅ SQL for Data Science 🗄️📊 👉 SQL is one of the most important skills for Data Scientists and Data Analysts. Almost every company stores data inside databases, and SQL helps retrieve and analyze that data. 🔹 1. What is SQL? SQL = Structured Query Language 👉 Used to: ✔ Store data ✔ Retrieve data ✔ Filter data ✔ Analyze data 🔥 2. Common Database Systems ✔ MySQL ✔ PostgreSQL ✔ SQLite ✔ Microsoft SQL Server 🔹 3. Basic SQL Query ✅ SELECT Statement Used to retrieve data from a table. SELECT * FROM employees; 👉 ** means all columns. 🔹 4. Select Specific Columns SELECT name, salary FROM employees; 🔹 5. WHERE Clause ⭐ Used for filtering data. SELECT * FROM employees WHERE salary > 50000; 🔹 6. ORDER BY Sort data. SELECT * FROM employees ORDER BY salary DESC; ✔ ASC → Ascending ✔ DESC → Descending 🔹 7. Aggregate Functions ⭐ Used for calculations. Function: COUNT() Purpose: Count rows Function: SUM() Purpose: Total Function: AVG() Purpose: Average Function: MAX() Purpose: Highest value Function: MIN() Purpose: Lowest value ✅ Example SELECT AVG(salary) FROM employees; 🔹 8. GROUP BY ⭐ Used to group data. SELECT department, AVG(salary) FROM employees GROUP BY department; 🔹 9. Why SQL is Important? ✔ Most asked interview skill ✔ Used daily by analysts & data scientists ✔ Essential for working with databases 🎯 Today’s Goal ✔ Learn SELECT queries ✔ Filter using WHERE ✔ Use aggregate functions ✔ Understand GROUP BY 👉 SQL Resources: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v 🗄️🔥 💬 Tap ❤️ for more!

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Here: loc = 50 represents the mean. scale = 10 represents the standard deviation. 🔹 17. Common Mistakes ❌ Confusing PMF and PDF → Remember: PMF → Discrete, PDF → Continuous ❌ Thinking PDF value is probability → For a continuous distribution, the PDF value at a point is a density, not the probability of that exact value. Probability comes from the area over an interval. ❌ Forgetting that CDF is cumulative → CDF always represents: P(X ≤ x) 🎯 Practice Questions 1. What is the difference between a discrete and continuous random variable? 2. What is PMF used for? 3. What does a PDF represent? 4. What does CDF calculate? 5. Name three probability distributions commonly used in Data Science. 🎯 Key Takeaways ✅ Probability distributions describe how probabilities are distributed across possible outcomes. ✅ Discrete variables have countable outcomes. ✅ Continuous variables can take infinitely many values within a range. ✅ PMF is used for discrete random variables. ✅ PDF is used for continuous random variables. ✅ CDF gives the cumulative probability up to a particular value. ✅ Normal, Binomial, and Poisson distributions are important distributions for Data Scientists. Understanding probability distributions gives you the foundation needed for statistical inference, hypothesis testing, machine learning, and advanced Data Science. 👉 Double Tap ❤️ For More ----- 2.42 ₽ · /balance_help

This tells us the probability that the score is 80 or less. 🔹 8. PMF vs PDF vs CDF PMF: Used for Discrete data. Represents Probability of an exact outcome PDF: Used for Continuous data. Represents Probability density CDF: Used for Discrete & continuous. Represents Probability up to a value A simple way to remember: PMF → Exact probability for discrete outcomes PDF → Density across continuous values CDF → Cumulative probability up to a value 🔹 9. Example: Discrete Distribution Suppose a machine produces defective products. Let: X = Number of defective products Possible values: 0, 1, 2, 3 Suppose: P(X=0) = 0.50 P(X=1) = 0.30 P(X=2) = 0.15 P(X=3) = 0.05 Check: 0.50 + 0.30 + 0.15 + 0.05 = 1.00 Therefore, this is a valid probability distribution. 🔹 10. Example: Continuous Distribution Suppose: X = Customer waiting time Waiting time could be: 2.1 minutes, 2.15 minutes, 2.157 minutes, 2.1578 minutes... Because there are infinitely many possible values, we treat it as a continuous random variable. A PDF can describe how densely the waiting times are distributed. 🔹 11. Normal Distribution ⭐ One of the most important probability distributions in Data Science is the Normal Distribution. It is often called the bell curve because of its shape. A normal distribution is characterized by: Mean, Standard deviation Many natural and measurement-related variables can be approximately normally distributed under suitable conditions. Examples: Measurement errors, Certain biological measurements, Standardized test scores 🔹 12. Properties of Normal Distribution For a perfectly symmetric normal distribution: Mean = Median = Mode The distribution is symmetric around its mean. A common rule of thumb is the 68–95–99.7 rule: Within 1 Standard Deviation: Approximately 68% Within 2 Standard Deviations: Approximately 95% Within 3 Standard Deviations: Approximately 99.7% 🔹 13. Binomial Distribution The Binomial Distribution is a discrete probability distribution used when: There are a fixed number of trials, Each trial has two possible outcomes, The probability of success is constant, Trials are independent. Examples: Number of successful predictions, Number of heads in coin tosses, Number of defective products in a fixed sample Example: 10 coin tosses. X = Number of Heads. Possible values: 0, 1, 2, ..., 10 🔹 14. Poisson Distribution The Poisson Distribution is commonly used to model the number of events occurring within a fixed interval when events occur at a certain average rate under appropriate assumptions. Examples: Number of customer calls per hour, Number of website visits per minute, Number of machine failures per month, Number of support tickets per day 🔹 15. Why Probability Distributions Matter in Data Science? Probability distributions help Data Scientists: ✅ Understand data patterns ✅ Detect unusual observations ✅ Model uncertainty ✅ Perform statistical tests ✅ Build predictive models ✅ Simulate data ✅ Estimate probabilities 🔹 16. Python Example

import numpy as np

data = np.random.normal(
    loc=50,
    scale=10,
    size=1000
)

print(data[:5])

🚀 Data Science Roadmap 2026 📘 Phase 2: Mathematics for Data Science 📖 Topic 6: Probability Distributions — Discrete, Continuous, PMF, PDF & CDF Welcome back! 👋 In the previous lesson, you learned Bayes' Theorem, which helps us update probabilities when new evidence becomes available. Now we'll learn Probability Distributions. Probability distributions are extremely important in Data Science because they help us understand how values are distributed and how likely different outcomes are. They are used in: ✅ Statistical analysis ✅ Machine Learning ✅ Hypothesis testing ✅ A/B testing ✅ Forecasting ✅ Risk analysis ✅ Data simulation 🔹 1. What is a Probability Distribution? A probability distribution describes how the probabilities of different possible outcomes are distributed. For example, when rolling a fair die: 1 → 1/6 2 → 1/6 3 → 1/6 4 → 1/6 5 → 1/6 6 → 1/6 Every possible outcome has an associated probability. The sum of all probabilities must equal: 1 = 100% 🔹 2. Two Main Types of Probability Distributions Probability distributions can broadly be divided into: 1️⃣ Discrete Distribution Used when outcomes are countable. Examples: Number of customers, Number of defective products, Number of emails, Number of heads in coin tosses 2️⃣ Continuous Distribution Used when values can take any value within a range. Examples: Height, Weight, Temperature, Time, Salary 🔹 3. Discrete Random Variable A discrete random variable takes countable values. Example: Number of customers arriving at a store: 0, 1, 2, 3, 4, 5, ... Another example: Number of defective products in a batch. 🔹 4. Continuous Random Variable A continuous random variable can take infinitely many possible values within a range. For example: someone's height could be: 170 cm, 170.1 cm, 170.15 cm, 170.157 cm... There are infinitely many possible values. 🔹 5. PMF — Probability Mass Function ⭐ PMF stands for: Probability Mass Function It is used for discrete random variables. PMF tells us the probability of a specific outcome. For example, when rolling a fair die: P(X=3) = 1/6 Important Rule: The probabilities of all possible outcomes must add up to 1: ∑P(X=x) = 1 🔹 6. PDF — Probability Density Function ⭐ PDF stands for: Probability Density Function It is used for continuous random variables. Unlike PMF, the PDF does not directly give the probability of a single exact value. Instead, the area under the PDF curve over an interval represents probability. For example: P(170 < Height < 180) is represented by the area under the PDF between 170 and 180. Important Point: For a continuous variable: P(X=x) = 0 for any exact single value under the usual continuous probability model. This doesn't mean the value is impossible. It means probability is assigned to intervals, not individual points. 🔹 7. CDF — Cumulative Distribution Function ⭐ CDF stands for: Cumulative Distribution Function It tells us the probability that a random variable is less than or equal to a particular value. Formula: F(x) = P(X ≤ x) Example: Suppose X = Test Score Then: F(80) = P(X ≤ 80)

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