Data Science & Machine Learning
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网络:📈 Telegram 频道 Data Science & Machine Learning 的分析概览
频道 Data Science & Machine Learning (@datasciencefun) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 77 277 名订阅者，在 教育 类别中位列第 2 020，并在 印度 地区排名第 4 066 位。
📊 受众指标与增长动态
自 невідомо 创建以来，项目保持高速增长，吸引了 77 277 名订阅者。
根据 25 八月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 432，过去 24 小时变化为 -7，整体触达仍然可观。
- 认证状态： 未认证
- 互动率 (ER)： 平均受众互动率为 2.60%。内容发布后 24 小时内通常能获得 1.13% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 2 008 次浏览，首日通常累积 876 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 3。
- 主题关注点： 内容集中在 learning, accuracy, distribution, panda, dataset 等核心主题上。
📝 描述与内容策略
作者将该频道定位为表达主观观点的平台：
“Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free
For collaborations: @love_data”
凭借高频更新（最新数据采集于 26 八月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为 教育 类别中的关键影响点。
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| 日期 | 订阅者增长 | 提及 | 频道 | |
| 26 八月 | +1 | |||
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| 23 八月 | +6 | |||
| 22 八月 | +14 | |||
| 21 八月 | +31 | |||
| 20 八月 | +13 | |||
| 19 八月 | +72 | |||
| 18 八月 | +6 | |||
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| 16 八月 | +39 | |||
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| 14 八月 | +18 | |||
| 13 八月 | +18 | |||
| 12 八月 | +13 | |||
| 11 八月 | +13 | |||
| 10 八月 | +19 | |||
| 09 八月 | +1 | |||
| 08 八月 | +11 | |||
| 07 八月 | +48 | |||
| 06 八月 | +7 | |||
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| 4 | Output: 1.0
This indicates a perfect positive linear relationship for this small example.
🔹 18. Common Mistakes
• Thinking correlation must be between 0 and 1 → Correlation can be negative: -1 <= r <= 1
• Thinking r = 0 means absolutely no relationship → It means there is no linear relationship detected by Pearson correlation. A nonlinear relationship may still exist.
• Assuming high correlation proves causation → Correlation only tells us that variables move together. It does not establish cause and effect.
🎯 Practice Questions
•
1. What does positive covariance indicate?
•
1. What is the possible range of Pearson correlation?
•
1. What does a correlation of -0.90 indicate?
•
1. Why is correlation easier to interpret than covariance?
•
1. Why doesn't correlation imply causation?
🎯 Key Takeaways
• Covariance measures how two variables change together.
• Positive covariance indicates that variables tend to move in the same direction.
• Negative covariance indicates that they tend to move in opposite directions.
•
Correlation measures the direction and strength of a linear relationship.
•
Pearson correlation ranges from -1 to +1.
• Correlation is unitless and easier to interpret than covariance.
• A correlation of +1 indicates perfect positive linear association.
• A correlation of -1 indicates perfect negative linear association.
• A correlation of 0 indicates no linear association.
• Correlation does not imply causation.
👉 Double Tap ❤️ For More 📊
-----
1.55 ₽ · /balance_help | 1 004 |
| 5 | • +0.9 → Very strong positive
• +0.5 → Moderate positive
• +0.1 → Weak positive
• 0 → No linear relationship
• -0.1 → Weak negative
• -0.5 → Moderate negative
• -0.9 → Very strong negative
The exact interpretation depends on the domain and context.
🔹 9. Pearson Correlation Coefficient ⭐
The most commonly used correlation measure is the Pearson correlation coefficient.
It is calculated as:
• r = Cov(X,Y) / (StdDev X ** StdDev Y)
Where:
• Cov(X,Y) = Covariance between X and Y
• StdDev X = Standard deviation of X
• StdDev Y = Standard deviation of Y
Because covariance is divided by the standard deviations, the result is standardized between -1 and +1.
🔹 10. Covariance vs Correlation
• Covariance: Measures direction of joint variation, Can have any numerical value, Depends on units, Harder to interpret, Useful mathematically
• Correlation: Measures direction and strength, Always between -1 and +1, Unitless, Easier to interpret, Very useful for EDA
🔹 11. Positive Correlation Example
Suppose: Advertising Spend ↑ → Sales ↑
If higher advertising spending generally corresponds to higher sales, the correlation may be positive.
• For example: r = 0.85 → This indicates a strong positive linear relationship.
🔹 12. Negative Correlation Example
Suppose: Price ↑ → Demand ↓
You might observe: r = -0.80 → This indicates a strong negative linear relationship.
🔹 13. Correlation Does NOT Mean Causation ⭐
This is one of the most important concepts in Data Science.
Suppose we observe: Ice Cream Sales ↑ ↔ Swimming Pool Accidents ↑
There may be a positive correlation. But eating ice cream doesn't necessarily cause swimming accidents.
A third variable — hot weather — could influence both:
• Hot Weather → Ice Cream Sales
• Hot Weather → Swimming Activity → Accidents
Therefore: Correlation does not prove causation.
🔹 14. Correlation and Machine Learning
Correlation is frequently used during Exploratory Data Analysis.
For example, suppose you're predicting house prices. You might examine correlations between:
• House size
• Number of bedrooms
• Location-related variables
• Age of property
• Price
A strong correlation between house size and price may indicate that house size could be a useful predictive feature.
However, correlation alone does not determine whether a feature should be included in a model.
🔹 15. Correlation Matrix ⭐
When a dataset contains many numerical variables, we can calculate correlations between every pair of variables. This produces a correlation matrix.
Example:
• Age | Income | Spending
• Age: 1.00, 0.65, -0.10
• Income: 0.65, 1.00, 0.72
• Spending: -0.10, 0.72, 1.00
The diagonal is always 1.00 because every variable has a perfect correlation with itself.
🔹 16. Detecting Multicollinearity
• Correlation can help identify multicollinearity.
• Multicollinearity occurs when two or more predictor variables are highly correlated with each other.
• For example: Annual Income ↔ Monthly Income — These variables contain very similar information.
• Including highly correlated predictors can create problems for some models, particularly linear regression, because it can make coefficient estimates unstable and harder to interpret.
🔹 17. Python Example
Using Pandas:
import pandas as pd
data = {
"Hours": [2, 4, 6, 8, 10],
"Score": [50, 60, 70, 80, 90]
}
df = pd.DataFrame(data)
print(df["Hours"].corr(df["Score"])) | 687 |
| 6 | 🚀 Data Science Roadmap 2026
📘 Phase 2: Mathematics for Data Science
📖 Topic 8: Covariance and Correlation
Welcome back! 👋
In the previous lesson, you learned about Range, Percentiles, Quartiles, IQR, and the Five-Number Summary.
Now let's learn two extremely important concepts for understanding relationships between variables:
• Covariance
• Correlation
These concepts are used extensively in Exploratory Data Analysis (EDA), feature selection, machine learning, and statistical analysis.
🔹 1. Why Do We Need Covariance and Correlation?
Suppose you're analyzing student data:
• Hours Studied | Exam Score
• 2 | 50
• 4 | 60
• 6 | 70
• 8 | 80
• 10 | 90
You can observe that as study hours increase, exam scores also increase.
But how can we mathematically measure this relationship?
That's where covariance and correlation come in.
🔹 2. What is Covariance?
Covariance measures the direction in which two variables change together.
It tells us whether two variables tend to increase or decrease together.
Three possibilities:
• Positive Covariance: When one variable increases, the other tends to increase. X ↑ → Y ↑. Example: Study hours ↑ → Exam score ↑
• Negative Covariance: When one variable increases, the other tends to decrease. X ↑ → Y ↓. Example: Product price ↑ → Demand ↓
• Covariance Near Zero: There is little or no linear relationship between the variables. X ↑ → No consistent change in Y
🔹 3. Covariance Formula
For population data:
• Cov(X,Y) = Sum of (Xi - Mean X) ** (Yi - Mean Y) / N
Where:
• Xi = Individual X value
• Yi = Individual Y value
• Mean X = Mean of X
• Mean Y = Mean of Y
• N = Number of observations
The calculation essentially asks: When X is above or below its average, is Y also above or below its average?
🔹 4. Simple Covariance Example
Consider:
• X = [1, 2, 3]
• Y = [2, 4, 6]
Means:
• Mean(X) = 2
• Mean(Y) = 4
Now calculate deviations:
• X | X - Mean X | Y | Y - Mean Y | Product
• 1 | -1 | 2 | -2 | 2
• 2 | 0 | 4 | 0 | 0
• 3 | 1 | 6 | 2 | 2
Sum of products: 2 + 0 + 2 = 4
Population covariance: Cov(X,Y) = 4 / 3 = 1.33
So covariance is positive. That makes sense because Y increases whenever X increases.
🔹 5. The Problem with Covariance
• Covariance tells us the direction of a relationship, but its magnitude depends on the units of the variables.
• For example: Height in centimeters, Weight in kilograms
• Changing centimeters to meters can change the numerical value of covariance.
• Therefore, covariance isn't always easy to interpret or compare.
• This leads us to correlation.
🔹 6. What is Correlation? ⭐
• Correlation measures both the direction and strength of a linear relationship between two variables.
• Unlike covariance, correlation is standardized.
• Its value always lies between: -1 <= r <= 1
🔹 7. Interpreting Correlation
• r = +1: Perfect positive linear relationship. X ↑ → Y ↑
• r = -1: Perfect negative linear relationship. X ↑ → Y ↓
• r = 0: No linear relationship.
• Important: r = 0 does not necessarily mean there is no relationship at all. A strong nonlinear relationship can still exist.
🔹 8. Correlation Strength
A rough interpretation: | 650 |
| 7 | Output: 1.0
This indicates a perfect positive linear relationship for this small example.
🔹 18. Common Mistakes
• Thinking correlation must be between 0 and 1 → Correlation can be negative: -1 <= r <= 1
• Thinking r = 0 means absolutely no relationship → It means there is no linear relationship detected by Pearson correlation. A nonlinear relationship may still exist.
• Assuming high correlation proves causation → Correlation only tells us that variables move together. It does not establish cause and effect.
🎯 Practice Questions
•
1. What does positive covariance indicate?
•
1. What is the possible range of Pearson correlation?
•
1. What does a correlation of -0.90 indicate?
•
1. Why is correlation easier to interpret than covariance?
•
1. Why doesn't correlation imply causation?
🎯 Key Takeaways
• Covariance measures how two variables change together.
• Positive covariance indicates that variables tend to move in the same direction.
• Negative covariance indicates that they tend to move in opposite directions.
•
Correlation measures the direction and strength of a linear relationship.
•
Pearson correlation ranges from -1 to +1.
• Correlation is unitless and easier to interpret than covariance.
• A correlation of +1 indicates perfect positive linear association.
• A correlation of -1 indicates perfect negative linear association.
• A correlation of 0 indicates no linear association.
• Correlation does not imply causation.
👉 Double Tap ❤️ For More 📊
-----
1.55 ₽ · /balance_help | 1 |
| 8 | • +0.9 → Very strong positive
• +0.5 → Moderate positive
• +0.1 → Weak positive
• 0 → No linear relationship
• -0.1 → Weak negative
• -0.5 → Moderate negative
• -0.9 → Very strong negative
The exact interpretation depends on the domain and context.
🔹 9. Pearson Correlation Coefficient ⭐
The most commonly used correlation measure is the Pearson correlation coefficient.
It is calculated as:
• r = Cov(X,Y) / (StdDev X ** StdDev Y)
Where:
• Cov(X,Y) = Covariance between X and Y
• StdDev X = Standard deviation of X
• StdDev Y = Standard deviation of Y
Because covariance is divided by the standard deviations, the result is standardized between -1 and +1.
🔹 10. Covariance vs Correlation
• Covariance: Measures direction of joint variation, Can have any numerical value, Depends on units, Harder to interpret, Useful mathematically
• Correlation: Measures direction and strength, Always between -1 and +1, Unitless, Easier to interpret, Very useful for EDA
🔹 11. Positive Correlation Example
Suppose: Advertising Spend ↑ → Sales ↑
If higher advertising spending generally corresponds to higher sales, the correlation may be positive.
• For example: r = 0.85 → This indicates a strong positive linear relationship.
🔹 12. Negative Correlation Example
Suppose: Price ↑ → Demand ↓
You might observe: r = -0.80 → This indicates a strong negative linear relationship.
🔹 13. Correlation Does NOT Mean Causation ⭐
This is one of the most important concepts in Data Science.
Suppose we observe: Ice Cream Sales ↑ ↔ Swimming Pool Accidents ↑
There may be a positive correlation. But eating ice cream doesn't necessarily cause swimming accidents.
A third variable — hot weather — could influence both:
• Hot Weather → Ice Cream Sales
• Hot Weather → Swimming Activity → Accidents
Therefore: Correlation does not prove causation.
🔹 14. Correlation and Machine Learning
Correlation is frequently used during Exploratory Data Analysis.
For example, suppose you're predicting house prices. You might examine correlations between:
• House size
• Number of bedrooms
• Location-related variables
• Age of property
• Price
A strong correlation between house size and price may indicate that house size could be a useful predictive feature.
However, correlation alone does not determine whether a feature should be included in a model.
🔹 15. Correlation Matrix ⭐
When a dataset contains many numerical variables, we can calculate correlations between every pair of variables. This produces a correlation matrix.
Example:
• Age | Income | Spending
• Age: 1.00, 0.65, -0.10
• Income: 0.65, 1.00, 0.72
• Spending: -0.10, 0.72, 1.00
The diagonal is always 1.00 because every variable has a perfect correlation with itself.
🔹 16. Detecting Multicollinearity
• Correlation can help identify multicollinearity.
• Multicollinearity occurs when two or more predictor variables are highly correlated with each other.
• For example: Annual Income ↔ Monthly Income — These variables contain very similar information.
• Including highly correlated predictors can create problems for some models, particularly linear regression, because it can make coefficient estimates unstable and harder to interpret.
🔹 17. Python Example
Using Pandas:
import pandas as pd
data = {
"Hours": [2, 4, 6, 8, 10],
"Score": [50, 60, 70, 80, 90]
}
df = pd.DataFrame(data)
print(df["Hours"].corr(df["Score"])) | 1 |
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| 10 | Which of the following represents the five-number summary? | 1 518 |
| 11 | Which formula is used to calculate the upper bound for potential outliers using the IQR method? | 1 400 |
| 12 | If Q1 = 20 and Q3 = 80, what is the IQR? | 1 255 |
| 13 | Which percentile represents the median? | 1 243 |
| 14 | What is the range of the dataset below?
10, 20, 30, 40, 50 | 1 205 |
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| 17 | Essential Excel Functions for Data Analysts 🚀
1️⃣ Basic Functions
SUM() – Adds a range of numbers. =SUM(A1:A10)
AVERAGE() – Calculates the average. =AVERAGE(A1:A10)
MIN() / MAX() – Finds the smallest/largest value. =MIN(A1:A10)
2️⃣ Logical Functions
IF() – Conditional logic. =IF(A1>50, "Pass", "Fail")
IFS() – Multiple conditions. =IFS(A1>90, "A", A1>80, "B", TRUE, "C")
AND() / OR() – Checks multiple conditions. =AND(A1>50, B1<100)
3️⃣ Text Functions
LEFT() / RIGHT() / MID() – Extract text from a string.
=LEFT(A1, 3) (First 3 characters)
=MID(A1, 3, 2) (2 characters from the 3rd position)
LEN() – Counts characters. =LEN(A1)
TRIM() – Removes extra spaces. =TRIM(A1)
UPPER() / LOWER() / PROPER() – Changes text case.
4️⃣ Lookup Functions
VLOOKUP() – Searches for a value in a column.
=VLOOKUP(1001, A2:B10, 2, FALSE)
HLOOKUP() – Searches in a row.
XLOOKUP() – Advanced lookup replacing VLOOKUP.
=XLOOKUP(1001, A2:A10, B2:B10, "Not Found")
5️⃣ Date & Time Functions
TODAY() – Returns the current date.
NOW() – Returns the current date and time.
YEAR(), MONTH(), DAY() – Extracts parts of a date.
DATEDIF() – Calculates the difference between two dates.
6️⃣ Data Cleaning Functions
REMOVE DUPLICATES – Found in the "Data" tab.
CLEAN() – Removes non-printable characters.
SUBSTITUTE() – Replaces text within a string.
=SUBSTITUTE(A1, "old", "new")
7️⃣ Advanced Functions
INDEX() & MATCH() – More flexible alternative to VLOOKUP.
TEXTJOIN() – Joins text with a delimiter.
UNIQUE() – Returns unique values from a range.
FILTER() – Filters data dynamically.
=FILTER(A2:B10, B2:B10>50)
8️⃣ Pivot Tables & Power Query
PIVOT TABLES – Summarizes data dynamically.
GETPIVOTDATA() – Extracts data from a Pivot Table.
POWER QUERY – Automates data cleaning & transformation.
You can find Free Excel Resources here: https://t.me/excel_data
Hope it helps :)
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