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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 76 441 подписчиков, занимая 2 066 место в категории Образование и 4 109 место в регионе Индия.

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С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 76 441 подписчиков.

Согласно последним данным от 13 июля, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 721, а за последние 24 часа — 38, при этом общий охват остаётся высоким.

Статус верификации: Не верифицирован
Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 2.82%. В первые 24 часа после публикации контент обычно набирает 1.30% реакций от общего числа подписчиков.
Охват публикаций: В среднем каждый пост получает 2 156 просмотров. В течение первых суток публикация набирает 992 просмотров.
Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 3.
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 14 июля, 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.

76 441

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Посты канала

if age >= 18:
    print("Eligible")

❌ Incorrect Indentation

if age >= 18:
print("Eligible")

Python requires proper indentation. Correct:

if age >= 18:
    print("Eligible")

🔹 11. Real-World Data Science Example

prediction = 0.82
if prediction >= 0.5:
    print("Spam Email")
else:
    print("Not Spam")

Many Machine Learning classification models use similar logic to convert prediction probabilities into categories. 🎯 Practice Questions 1. Check whether a number is positive or negative. 2. Check whether a person is eligible to vote. 3. Create a grading system using "if...elif...else". 4. Check whether a number is even or odd. 5. Determine the largest of three numbers. 🎯 Key Takeaways ✅ Use "if" to execute code when a condition is True. ✅ Use "else" to execute code when the condition is False. ✅ Use "elif" to check multiple conditions. ✅ Nested "if" statements allow more complex decision-making. ✅ Logical operators (and, or, not) help combine conditions. ✅ The ternary operator provides a concise way to write simple "if...else" statements. Conditional statements are the foundation of decision-making in Python and are widely used in automation, data analysis, machine learning, and AI applications. Double Tap ❤️ For Part-5 ----- 1.35 ₽ · /balance_help

2	🚀 Data Science Roadmap 2026 📘 Phase 1: Programming Fundamentals 🐍 Topic 4: Python Conditional Statements (if, elif, else) Welcome back! 👋 In the previous lesson, you learned how to take input from users and display output. Now it's time to learn how Python makes decisions. Conditional statements allow a program to execute different blocks of code based on whether a condition is True or False. They are one of the most important concepts in Python and are used extensively in Data Science, Machine Learning, and real-world applications. 🔹 1. What are Conditional Statements? Conditional statements help Python decide which code should run based on a condition. Think of it like making everyday decisions: • If it's raining → Carry an umbrella. • Else → Wear sunglasses. Similarly, Python checks a condition and decides what to execute. 🔹 2. The "if" Statement The "if" statement executes a block of code only if the condition is True. Syntax if condition: # Code to execute Example age = 20 if age >= 18: print("Eligible to vote") Output Eligible to vote 🔹 3. The "if...else" Statement Use "else" when you want to execute another block if the condition is False. Example age = 16 if age >= 18: print("Eligible to vote") else: print("Not eligible to vote") Output Not eligible to vote 🔹 4. The "if...elif...else" Statement ⭐ Use "elif" when you need to check multiple conditions. Example marks = 85 if marks >= 90: print("Grade A") elif marks >= 75: print("Grade B") elif marks >= 60: print("Grade C") else: print("Grade D") Output Grade B Python checks conditions from top to bottom and executes the first condition that is True. 🔹 5. Nested "if" Statements You can place one "if" statement inside another. Example age = 25 citizen = True if age >= 18: if citizen: print("Eligible to vote") Output Eligible to vote 🔹 6. Using Logical Operators Conditional statements often use logical operators. "and" age = 25 if age >= 18 and age <= 60: print("Working Age") "or" marks = 35 if marks >= 40 or marks == 35: print("Eligible for Grace Marks") "not" is_holiday = False if not is_holiday: print("Go to Office") 🔹 7. Checking Multiple Conditions salary = 60000 experience = 4 if salary > 50000 and experience >= 3: print("Eligible for Promotion") else: print("Not Eligible") 🔹 8. Ternary Operator ⭐ A shorter way to write an "if...else" statement. Syntax value_if_true if condition else value_if_false Example age = 20 status = "Adult" if age >= 18 else "Minor" print(status) Output Adult 🔹 9. Real-World Example temperature = 38 if temperature > 35: print("It's a hot day.") elif temperature >= 20: print("Weather is pleasant.") else: print("It's cold.") 🔹 10. Common Mistakes ❌ Missing Colon if age >= 18 print("Eligible") This gives a SyntaxError. Correct:	720
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5	Which is the recommended way to format strings in modern Python?	1 463
6	Which function should you use to convert user input into an integer?	1 368
7	What data type does the input() function return by default?	1 279
8	Which function is used to take input from the user in Python?	1 203
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11	num1 = int(input("Enter first number: ")) num2 = int(input("Enter second number: ")) print(num1 + num2) Output: 30 🔹 12. Real-World Example salary = float(input("Enter your monthly salary: ")) annual_salary = salary * 12 print(f"Your annual salary is {annual_salary}") 🎯 Practice Questions 1. Take your name as input and print a welcome message. 2. Take two integers as input and print their sum. 3. Take a student's marks as input and print them using an f-string. 4. Take the radius of a circle as input and calculate the area. 5. Take your birth year as input and calculate your approximate age. 🎯 Key Takeaways ✅ Use print() to display output ✅ Use input() to accept user input ✅ input() always returns a string ✅ Convert input using int() or float() when needed ✅ Use f-strings for clean and readable output formatting Double Tap ❤️ For More	1 538
12	🚀 Data Science Roadmap 2026 📘 Phase 1: Programming Fundamentals 🐍 Topic 3: Python Input & Output In the previous lesson, you learned about Python Operators. Now it's time to learn how Python interacts with users by taking input and displaying output. Input and Output (I/O) are fundamental concepts because almost every real-world program accepts input, processes it, and produces meaningful output. 🔹 1. What is Input & Output? A Python program generally follows three steps: Input → Process → Output Example: • User enters two numbers Input • Python adds them Process • Sum is displayed Output 🔹 2. Displaying Output Python uses the print() function to display information on the screen. Example print("Hello, Data Science!") Output Hello, Data Science! 🔹 3. Printing Variables You can print variables along with text. name = "Deepak" print(name) Output: Deepak Or: name = "Deepak" print("Welcome", name) Output: Welcome Deepak 🔹 4. Taking User Input Python uses the input() function to receive input from users. name = input("Enter your name: ") print("Hello", name) Example Output: Enter your name: Deepak Hello Deepak 🔹 5. Important Note ⭐ The input() function always returns a string, even if the user enters a number. age = input("Enter age: ") print(type(age)) Output: <class 'str'> 🔹 6. Converting Input to Integer To perform mathematical operations, convert the input using int(). age = int(input("Enter your age: ")) print(age + 5) Example: Enter your age: 25 30 🔹 7. Taking Decimal Input Use float() for decimal numbers. price = float(input("Enter price: ")) print(price) 🔹 8. Taking Multiple Inputs You can take multiple inputs in a single line. name, city = input("Enter your name and city: ").split() print(name) print(city) Example Input: Deepak Mumbai Output: Deepak Mumbai 🔹 9. Formatting Output Using f-Strings ⭐ Recommended name = "Deepak" age = 25 print(f"My name is {name} and I am {age} years old.") Output: My name is Deepak and I am 25 years old. Using .format() name = "Deepak" print("Welcome {}".format(name)) 🔹 10. Example Program name = input("Enter your name: ") age = int(input("Enter your age: ")) print(f"Hello {name}") print(f"Next year you will be {age + 1} years old.") Example Output: Enter your name: Deepak Enter your age: 25 Hello Deepak Next year you will be 26 years old. 🔹 11. Common Mistake num1 = input("Enter first number: ") num2 = input("Enter second number: ") print(num1 + num2) Input: 10 20 Output: 1020 Why? Because both values are strings. Correct way:	1 248
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14	Which logical operator returns True only if both conditions are True?	1 685
15	Which operator is used for exponentiation (power) in Python?	1 644
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17	result = 10 + 5 * 2 print(result) Output: 20 Multiplication is performed before addition. Use parentheses to change the order. result = (10 + 5) * 2 print(result) Output: 30 🔹 10. Real-World Example salary = 60000 bonus = 5000 total_salary = salary + bonus is_high_salary = total_salary > 50000 print(total_salary) print(is_high_salary) Output: 65000 True 🎯 Key Takeaways ✅ Operators perform calculations and comparisons. ✅ Arithmetic operators are used for mathematical operations. ✅ Comparison operators return True or False. ✅ Logical operators help combine multiple conditions. ✅ Membership operators check if a value exists in a sequence. ✅ Identity operators check whether two variables refer to the same object. Double Tap ❤️ For More	1 646
18	🚀 Data Science Roadmap 2026 📘 Phase 1: Programming Fundamentals 🐍 Topic 2: Python Operators In the previous lesson, you learned about Variables & Data Types. Now it's time to learn how Python performs calculations, comparisons, and logical operations using operators. Operators are one of the most fundamental concepts in Python. You'll use them in almost every program, from simple calculations to complex Machine Learning algorithms. 🔹 1. What are Operators? Operators are special symbols used to perform operations on variables and values. Example: a = 10 b = 5 print(a + b) Output: 15 Here, "+" is an operator that adds two numbers. 🔹 2. Types of Operators in Python Python has several types of operators: ✅ Arithmetic Operators ✅ Comparison Operators ✅ Assignment Operators ✅ Logical Operators ✅ Membership Operators ✅ Identity Operators 🔹 3. Arithmetic Operators ⭐ Used for mathematical calculations. Operators: • + Addition: 10 + 5 = 15 • - Subtraction: 10 - 5 = 5 • ** Multiplication: 10 5 = 50 • / Division: 10 / 5 = 2.0 • // Floor Division: 10 // 3 = 3 • % Modulus (Remainder): 10 % 3 = 1 • Exponent: 2 3 = 8 Example: a = 10 b = 3 print(a + b) print(a - b) print(a * b) print(a / b) print(a // b) print(a % b) print(a ** b) 🔹 4. Comparison Operators ⭐ Used to compare two values. The result is always True or False. Operators: • == Equal to • != Not Equal to • > Greater than • < Less than • >= Greater than or Equal to • <= Less than or Equal to Example: x = 20 y = 10 print(x > y) print(x == y) print(x != y) Output: True False True 🔹 5. Assignment Operators Used to assign values to variables. x = 10 x += 5 print(x) Output: 15 Other assignment operators: x -= 2 x *= 3 x /= 2 🔹 6. Logical Operators ⭐ Used to combine multiple conditions. and Returns True only if both conditions are True. age = 25 print(age > 18 and age < 30) Output: True or Returns True if at least one condition is True. print(age < 18 or age < 30) Output: True not Reverses the result. print(not(age > 18)) Output: False 🔹 7. Membership Operators Used to check whether a value exists in a sequence. in fruits = ["Apple", "Banana", "Mango"] print("Apple" in fruits) Output: True not in print("Orange" not in fruits) Output: True 🔹 8. Identity Operators Used to check whether two variables refer to the same object. is a = [1, 2] b = a print(a is b) Output: True is not x = [1, 2] y = [1, 2] print(x is not y) Output: True 🔹 9. Operator Precedence Python follows the PEMDAS/BODMAS rule while evaluating expressions. Example:	1 350
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20	GigaChat 3.5 Ultra Publicly Released — The New Generation of the Flagship Model The GigaChat team has released GigaChat 3.5 Ultra as open source—a new 432B model under the MIT license. This is the first open-source hybrid of GatedDeltaNet and MLA scaled to hundreds of billions of parameters, featuring a proprietary training recipe we refined through more than 1,500 experiments. The model has grown in terms of code, mathematics, agent scenarios, and application domains—yet it’s 40% smaller than GigaChat 3.1 Ultra. What’s inside: 🔘A proprietary hybrid MLA + Gated DeltaNet architecture with a dedicated stabilization framework, without which this hybrid setup would not train reliably at this scale; 🔘 Gated Attention: the model can locally down-weight overly strong signals from the attention layer; 🔘GatedNorm: normalization with an explicit gate that controls signal magnitude across features; 🔘Approximately 4x lower KV cache per token: with the same memory budget, the model can support 2.14x longer context and deliver a 20% throughput increase under load; 🔘Two MTP heads, enabling up to 2.2x faster generation; 🔘FP8 across all training stages with no quality degradation compared with bf16, enabled by custom Triton and CUDA kernels; 🔘A new online RL stage after SFT and DPO. Results: 🔘 GigaChat-3.5-Ultra-Base outperforms DeepSeek V3.2 Exp Base and DeepSeek V4 Flash Base on average across a set of general, math, and code benchmarks: 🔘 GigaChat-3.5-Ultra-Instruct is comparable to DeepSeek V3.2 in terms of average score, despite having half the size; 🔘 According to the MiniMax-M2.7 LLM judge, the average win rate against GigaChat 3.1 Ultra is 75.9%, and against GPT-5 is 68.7%. The entire stack — data (our own LLM-filtered Common Crawl, 600+ programming languages in the code), architecture, training methodology, and infrastructure — was built end-to-end by GigaChat team. ➡️ HuggingFace	1 486

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