Data Science & Machine Learning
Відкрити в Telegram
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
Показати більше
Сітка:📈 Аналітичний огляд Telegram-каналу Data Science & Machine Learning
Канал Data Science & Machine Learning (@datasciencefun) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 77 277 підписників, посідаючи 2 020 місце в категорії Освіта та 4 066 місце у регіоні Індія.
📊 Показники аудиторії та динаміка
З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 77 277 підписників.
За останніми даними від 25 серпня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 432, а за останні 24 години на -7, загальне охоплення залишається високим.
- Статус верифікації: Не верифікований
- Рівень залученості (ER): Середній показник залученості аудиторії становить 2.60%. Протягом перших 24 годин після публікації контент зазвичай збирає 1.13% реакцій від загальної кількості підписників.
- Охоплення публікацій: В середньому кожен допис отримує 2 008 переглядів. Протягом першої доби публікація в середньому набирає 876 переглядів.
- Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 3.
- Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як learning, accuracy, distribution, panda, dataset.
📝 Опис та контентна політика
Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“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”
Завдяки високій частоті оновлень (останні дані отримано 26 серпня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Освіта.
77 277
Підписники
-724 години
+887 днів
+43230 день
Триває завантаження даних...
Схожі канали
Хмара тегів
Вхідні та вихідні згадування
---
---
---
---
---
---
Залучення підписників
серпень '26
серпень '26
+454
в 11 каналах
липень '26
+932
в 15 каналах
Get PRO
червень '26
+859
в 15 каналах
Get PRO
травень '26
+1 098
в 10 каналах
Get PRO
квітень '26
+629
в 14 каналах
Get PRO
березень '26
+315
в 4 каналах
Get PRO
лютий '26
+843
в 12 каналах
Get PRO
січень '26
+1 339
в 18 каналах
Get PRO
грудень '25
+1 264
в 6 каналах
Get PRO
листопад '25
+1 415
в 14 каналах
Get PRO
жовтень '25
+1 119
в 18 каналах
Get PRO
вересень '25
+598
в 24 каналах
Get PRO
серпень '25
+2 431
в 24 каналах
Get PRO
липень '25
+2 317
в 36 каналах
Get PRO
червень '25
+1 379
в 41 каналах
Get PRO
травень '25
+2 702
в 49 каналах
Get PRO
квітень '25
+4 574
в 35 каналах
Get PRO
березень '25
+1 830
в 36 каналах
Get PRO
лютий '25
+1 553
в 36 каналах
Get PRO
січень '25
+1 334
в 55 каналах
Get PRO
грудень '24
+1 276
в 32 каналах
Get PRO
листопад '24
+2 847
в 28 каналах
Get PRO
жовтень '24
+3 775
в 23 каналах
Get PRO
вересень '24
+3 717
в 24 каналах
Get PRO
серпень '24
+3 956
в 21 каналах
Get PRO
липень '24
+3 673
в 32 каналах
Get PRO
червень '24
+3 950
в 34 каналах
Get PRO
травень '24
+3 398
в 37 каналах
Get PRO
квітень '24
+2 919
в 23 каналах
Get PRO
березень '24
+3 461
в 14 каналах
Get PRO
лютий '24
+2 792
в 9 каналах
Get PRO
січень '24
+3 714
в 11 каналах
Get PRO
грудень '23
+2 830
в 7 каналах
Get PRO
листопад '23
+1 010
в 12 каналах
Get PRO
жовтень '23
+474
в 9 каналах
Get PRO
вересень '23
+409
в 0 каналах
Get PRO
серпень '23
+946
в 0 каналах
Get PRO
липень '23
+875
в 0 каналах
Get PRO
червень '23
+744
в 0 каналах
Get PRO
травень '23
+372
в 0 каналах
Get PRO
квітень '23
+577
в 0 каналах
Get PRO
березень '23
+253
в 0 каналах
Get PRO
лютий '23
+263
в 0 каналах
Get PRO
січень '23
+287
в 0 каналах
Get PRO
грудень '22
+303
в 0 каналах
Get PRO
листопад '22
+400
в 0 каналах
Get PRO
жовтень '22
+275
в 0 каналах
Get PRO
вересень '22
+143
в 0 каналах
Get PRO
серпень '22
+383
в 0 каналах
Get PRO
липень '22
+304
в 0 каналах
Get PRO
червень '22
+142
в 0 каналах
Get PRO
травень '22
+125
в 0 каналах
Get PRO
квітень '22
+107
в 0 каналах
Get PRO
березень '22
+190
в 0 каналах
Get PRO
лютий '22
+147
в 0 каналах
Get PRO
січень '22
+291
в 0 каналах
Get PRO
грудень '21
+289
в 0 каналах
Get PRO
листопад '21
+19
в 0 каналах
Get PRO
жовтень '21
+39
в 0 каналах
Get PRO
вересень '21
+274
в 0 каналах
Get PRO
серпень '21
+168
в 0 каналах
Get PRO
липень '21
+323
в 0 каналах
Get PRO
червень '21
+360
в 0 каналах
Get PRO
травень '21
+382
в 0 каналах
Get PRO
квітень '21
+327
в 0 каналах
Get PRO
березень '21
+434
в 0 каналах
Get PRO
лютий '21
+449
в 0 каналах
Get PRO
січень '21
+480
в 0 каналах
Get PRO
грудень '20
+1 208
в 0 каналах
| Дата | Залучення підписників | Згадування | Канали | |
| 26 серпня | +1 | |||
| 25 серпня | +6 | |||
| 24 серпня | +1 | |||
| 23 серпня | +6 | |||
| 22 серпня | +14 | |||
| 21 серпня | +31 | |||
| 20 серпня | +13 | |||
| 19 серпня | +72 | |||
| 18 серпня | +6 | |||
| 17 серпня | +3 | |||
| 16 серпня | +39 | |||
| 15 серпня | +6 | |||
| 14 серпня | +18 | |||
| 13 серпня | +18 | |||
| 12 серпня | +13 | |||
| 11 серпня | +13 | |||
| 10 серпня | +19 | |||
| 09 серпня | +1 | |||
| 08 серпня | +11 | |||
| 07 серпня | +48 | |||
| 06 серпня | +7 | |||
| 05 серпня | +23 | |||
| 04 серпня | +10 | |||
| 03 серпня | +36 | |||
| 02 серпня | +28 | |||
| 01 серпня | +11 |
Дописи каналу
🎓 𝐀𝐜𝐜𝐞𝐧𝐭𝐮𝐫𝐞 𝐅𝐑𝐄𝐄 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 😍
Boost your skills with 100% FREE certification courses from Accenture!
📚 FREE Courses Offered:
1️⃣ Data Processing and Visualization
2️⃣ Exploratory Data Analysis
3️⃣ SQL Fundamentals
4️⃣ Python Basics
5️⃣ Acquiring Data
𝐋𝐢𝐧𝐤 👇:-
https://pdlink.in/4yJKnBy
✅ Learn Online | 📜 Get Certified
| 2 |  𝗙𝗥𝗘𝗘 𝗚𝗲𝗻𝗔𝗜 + 𝗖𝗹𝗮𝘂𝗱𝗲 𝗢𝗻𝗹𝗶𝗻𝗲 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀😍
Learn how to use 25+ powerful AI tools to automate your work, create professional content and save hours every week!
🎯 Perfect For:-
Freelancers • Working Professionals • Business Owners • Self-Employed Individuals
💡 No technical knowledge or prior experience required!
🔗 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 👇:-
https://pdlinks.in/ai
⚡ Start using AI smarter—limited slots available! | 556 |
| 3 |  Your Data Science degree just got an AI update.
Yeah.
Things are moving fast.
Python. SQL. Machine Learning. Deep Learning. MLOps.
And now GenAI, LLMs, RAG & AI-powered workflows.
An 8-month program with 20+ industry projects and live weekend classes.
Maybe Data Science was just the beginning.
https://lp.pwskills.com/data-science-ai-online-program-pw-skills?utm_source=telegram&utm_medium=influencer&utm_campaign=deepakAugDS | 1 035 |
| 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 |
| 9 |  🚀 𝗙𝗥𝗘𝗘 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲! 📊
Here’s a great chance to learn valuable skills and earn a FREE Certificate 🎓
✅ Beginner-friendly
✅ Learn Data Analytics skills
✅ Free certification
✅ Boost your resume & LinkedIn profile
✅ Great for students & job seekers
𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇 :-
https://pdlink.in/4qn5q94
📌 Start learning today & upgrade your career! | 1 012 |
| 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 |
| 15 |  𝗣𝗮𝘆 𝗔𝗳𝘁𝗲𝗿 𝗣𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁—𝗕𝗲𝗰𝗼𝗺𝗲 𝗮 𝗙𝘂𝗹𝗹 𝗦𝘁𝗮𝗰𝗸 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝘄𝗶𝘁𝗵 𝗚𝗲𝗻𝗔𝗜😍
Curriculum designed and taught by alumni from IITs & leading tech companies.
🏆 Placement Highlights:-
💰 ₹41 LPA highest salary
📈 ₹7.4 LPA average salary
🎓 2,000+ students placed
🏢 500+ partner companies
🔗 𝗔𝗽𝗽𝗹𝘆 𝗡𝗼𝘄 👇:-
https://pdlink.in/3SuUeuD
⚡ Take the first step toward your dream tech career today! | 1 285 |
| 16 |  🚀 𝗪𝗶𝗽𝗿𝗼 𝗘𝗹𝗶𝘁𝗲 𝗡𝗧𝗛 & 𝗧𝘂𝗿𝗯𝗼 𝗙𝗥𝗘𝗘 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗞𝗶𝘁 💻🔥
Get access to a FREE interview preparation kit and prepare smarter for your upcoming assessment & interview rounds.
📚 Prepare For:-
✅ Technical Interview Questions
✅ Software Engineer Interview Rounds
✅ Interview Preparation Resources
🎯 Perfect for Students | Freshers | Engineering Graduates | Wipro Aspirants
🔗 𝗚𝗲𝘁 𝗙𝗥𝗘𝗘 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗞𝗶𝘁 👇:-
https://pdlink.in/4zh9E6g
🔥 Start preparing early and improve your chances of cracking the Wipro hiring process! | 1 419 |
| 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 :)
#dataanalytics | 1 594 |
| 18 |  🚀 𝗔𝗜 & 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲
🔥 Upgrade your skills and prepare for exciting career opportunities in AI!
✅ Beginner-friendly course
✅ Learn AI & Machine Learning fundamentals
✅ Gain practical, job-ready skills
✅ Earn a FREE certificate
✅ Boost your resume and LinkedIn profile
✅ Ideal for students, freshers and professionals
🔗 𝗘𝗻𝗿𝗼𝗹𝗹 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 👇:-
https://pdlink.in/4zrkYNg
⚡ Limited opportunity—start learning today! | 1 325 |
| 19 |  ☁️ 𝟰 𝗙𝗥𝗘𝗘 𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗹𝗼𝘂𝗱 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 | 𝗕𝘂𝗶𝗹𝗱 𝗜𝗻-𝗗𝗲𝗺𝗮𝗻𝗱 𝗖𝗹𝗼𝘂𝗱 𝗦𝗸𝗶𝗹𝗹𝘀
Explore these Google Cloud learning resources covering cloud fundamentals, infrastructure, networking, security, data and AI/ML.
🔥 4 Courses to Explore:
1️⃣ Cloud Computing Fundamentals
2️⃣ Infrastructure in Google Cloud
3️⃣ Networking & Security in Google Cloud
4️⃣ Data, ML & AI in Google Cloud
🔗 𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:-
https://pdlink.in/4zrksPn
🎯 Perfect for Students | Freshers | Developers | Cloud & DevOps Aspirants | 1 513 |
| 20 |  𝗪𝗢𝗥𝗞 𝗙𝗥𝗢𝗠 𝗛𝗢𝗠𝗘 𝗝𝗢𝗕 𝗢𝗣𝗣𝗢𝗥𝗧𝗨𝗡𝗜𝗧𝗬 😍
Company Name :- AI InsurTech Company
💼 𝗥𝗼𝗹𝗲: Backend Developer
💰 𝗦𝗮𝗹𝗮𝗿𝘆: ₹5 LPA
🏠 𝗪𝗼𝗿𝗸 𝗠𝗼𝗱𝗲: Work From Home
📍 𝗟𝗼𝗰𝗮𝘁𝗶𝗼𝗻: Hyderabad / Remote
🎓 𝗪𝗵𝗼 𝗖𝗮𝗻 𝗔𝗽𝗽𝗹𝘆?
✅ BTech/BE graduates
✅ Branches: CS, IT, AI, ML and Data-related streams
✅ Graduation Years: 2025 and 2026
🔗 𝗔𝗽𝗽𝗹𝘆 𝗡𝗼𝘄 👇:-
https://pdlink.in/4xIfsE4
⚡ Apply early and share this opportunity with your friends! | 1 948 |
