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📈 Аналитический обзор Telegram-канала Data Science & Machine Learning

Канал Data Science & Machine Learning (@datasciencefun) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 75 645 подписчиков, занимая 2 114 место в категории Образование и 4 359 место в регионе Индия.

📊 Показатели аудитории и динамика

С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 75 645 подписчиков.

Согласно последним данным от 11 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 911, а за последние 24 часа — 29, при этом общий охват остаётся высоким.

Статус верификации: Не верифицирован
Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 3.63%. В первые 24 часа после публикации контент обычно набирает 1.36% реакций от общего числа подписчиков.
Охват публикаций: В среднем каждый пост получает 2 747 просмотров. В течение первых суток публикация набирает 1 032 просмотров.
Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 5.
Тематические интересы: Контент сосредоточен на ключевых темах, таких как learning, accuracy, distribution, panda, dataset.

📝 Описание и контентная политика

Автор описывает ресурс как площадку для выражения субъективного мнения:
“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”

Благодаря высокой частоте обновлений (последние данные получены 12 июня, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Образование.

75 645

Подписчики

+2924 часа

+2107 дней

+91130 день

2 747

Просмотры поста

~ 1 03224 часа

~ 1 40648 часов

3.63%

Коэффициент вовлеченности

~ 2

Постов в день

Ads index

beta

Архив постов

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Which symbol is used to create a dictionary in Python?

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Now, let's move to the next topic of Data Science Roadmap: ✅ Python Dictionaries 📚 Dictionaries are one of the most important data structures in Python, especially in data science and real-world datasets. They store data in key–value pairs. 🔹 1. What is a Dictionary? A dictionary stores data in key:value format. ✅ Example:

student = { "name": "Rahul", "age": 22, "course": "Data Science" }
print(student)

Output: {'name': 'Rahul', 'age': 22, 'course': 'Data Science'} ✔ Uses curly brackets {} 🔹 2. Access Dictionary Values Use the key to access values.

student = { "name": "Rahul", "age": 22 }
print(student["name"])

Output: Rahul 🔹 3. Add New Elements

student = { "name": "Rahul", "age": 22 }
student["city"] = "Delhi"
print(student)

Output: {'name': 'Rahul', 'age': 22, 'city': 'Delhi'} 🔹 4. Modify Values

student["age"] = 23

🔹 5. Remove Elements

student.pop("age")

🔹 6. Important Dictionary Methods ⭐ ✅ Get Method:

print(student.get("name"))

Output: Rahul ✅ Keys Method:

print(student.keys())

Output: dict_keys(['name', 'age']) ✅ Values Method:

print(student.values())

Output: dict_values(['Rahul', 22]) ✅ Items Method:

print(student.items())

Output: dict_items([('name', 'Rahul'), ('age', 22)]) 🔹 7. Loop Through Dictionary

student = { "name": "Rahul", "age": 22 }

for key, value in student.items():
    print(key, value)

Output: name Rahul age 22 🎯 Today’s Goal ✔ Understand key–value pairs ✔ Access dictionary values ✔ Add or update data ✔ Loop through dictionary 👉 Dictionaries are widely used in APIs, JSON data, and machine learning datasets. Double Tap ♥️ For More

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What will be the output? age = 16 print("Adult") if age >= 18 else print("Minor")

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🔹 Q4. What will be the output? x = 7 if x > 10: print("A") elif x > 5: print("B") else: print("C")

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Which keyword is used to check multiple conditions?

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What will be the output? x = 10 if x > 5: print("Yes") else: print("No")

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Which keyword is used to check a condition in Python?

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Data Science Roadmap ✅ Conditional Statements (if–else) 🐍⚡ Conditional statements allow programs to make decisions based on conditions. 👉 Used heavily in: ✔ Data filtering ✔ Business rules ✔ Machine learning logic 🔹 1. if Statement Used to execute code when a condition is True. ✅ Syntax

if condition:
    # code

Example

age = 20
if age >= 18:
    print("You can vote")

# Output: You can vote 🔹 2. if–else Statement Used when there are two possible outcomes. Syntax

if condition:
    # code if true
else:
    # code if false

Example

age = 16
if age >= 18:
    print("Eligible to vote")
else:
    print("Not eligible")

🔹 3. if–elif–else Statement Used when there are multiple conditions. Syntax

if condition1:
    # code
elif condition2:
    # code
else:
    # code

Example

marks = 75
if marks >= 90:
    print("Grade A")
elif marks >= 60:
    print("Grade B")
else:
    print("Grade C")

🔹 4. Nested if Statement An if statement inside another if.

age = 20
citizen = True
if age >= 18:
    if citizen:
        print("Eligible to vote")

🔹 5. Short if (Ternary Operator)

age = 20
print("Adult") if age >= 18 else print("Minor")

🎯 Today’s Goal ✔ Understand if ✔ Use if–else ✔ Use elif for multiple conditions ✔ Learn nested conditions 👉 Conditional logic is used in data filtering and decision models. Double Tap ♥️ For More

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🔍 Machine Learning Cheat Sheet 🔍 1. Key Concepts: - Supervised Learning: Learn from labeled data (e.g., classification, regression). - Unsupervised Learning: Discover patterns in unlabeled data (e.g., clustering, dimensionality reduction). - Reinforcement Learning: Learn by interacting with an environment to maximize reward. 2. Common Algorithms: - Linear Regression: Predict continuous values. - Logistic Regression: Binary classification. - Decision Trees: Simple, interpretable model for classification and regression. - Random Forests: Ensemble method for improved accuracy. - Support Vector Machines: Effective for high-dimensional spaces. - K-Nearest Neighbors: Instance-based learning for classification/regression. - K-Means: Clustering algorithm. - Principal Component Analysis(PCA) 3. Performance Metrics: - Classification: Accuracy, Precision, Recall, F1-Score, ROC-AUC. - Regression: Mean Absolute Error (MAE), Mean Squared Error (MSE), R^2 Score. 4. Data Preprocessing: - Normalization: Scale features to a standard range. - Standardization: Transform features to have zero mean and unit variance. - Imputation: Handle missing data. - Encoding: Convert categorical data into numerical format. 5. Model Evaluation: - Cross-Validation: Ensure model generalization. - Train-Test Split: Divide data to evaluate model performance. 6. Libraries: - Python: Scikit-Learn, TensorFlow, Keras, PyTorch, Pandas, Numpy, Matplotlib. - R: caret, randomForest, e1071, ggplot2. 7. Tips for Success: - Feature Engineering: Enhance data quality and relevance. - Hyperparameter Tuning: Optimize model parameters (Grid Search, Random Search). - Model Interpretability: Use tools like SHAP and LIME. - Continuous Learning: Stay updated with the latest research and trends. 🚀 Dive into Machine Learning and transform data into insights! 🚀 Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624 All the best 👍👍

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✅ Python Functions 🐍⚙️ Functions are very important in data science. They help you write reusable, clean, and modular code. 🔹 1. What is a Function? A function is a block of code that performs a specific task. 👉 Instead of writing the same code again and again, we create a function. 🔥 2. Creating a Function ✅ Basic Syntax

def function_name():
    # code

✅ Example

def greet():
    print("Hello Deepak")
greet()

Output: Hello Deepak 🔹 3. Function with Parameters Parameters allow input to functions.

def greet(name):
    print("Hello", name)
greet("Rahul")

# Output: Hello Rahul 🔹 4. Function with Return Value (Very Important ⭐) Instead of printing, functions can return values.

def add(a, b):
    return a + b
result = add(5, 3)
print(result)

# Output: 8 👉 return sends value back. 🔹 5. Default Parameters

def greet(name="Guest"):
    print("Hello", name)
greet()
greet("Amit")

🔹 6. Why Functions Matter in Data Science? ✅ Data cleaning functions ✅ Feature engineering functions ✅ Reusable ML pipelines ✅ Code organization 🎯 Today’s Goal ✔ Understand def ✔ Use parameters ✔ Use return ✔ Call functions properly Double Tap ♥️ For More

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Which function generates a sequence of numbers for looping?

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What happens if we don’t update the condition inside a while loop?

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What will be the output? i = 1 while i < 3: print(i) i += 1

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