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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📈 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 75 676 підписників, посідаючи 2 114 місце в категорії Освіта та 4 348 місце у регіоні Індія.
📊 Показники аудиторії та динаміка
З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 75 676 підписників.
За останніми даними від 12 червня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 923, а за останні 24 години на 31, загальне охоплення залишається високим.
- Статус верифікації: Не верифікований
- Рівень залученості (ER): Середній показник залученості аудиторії становить 3.63%. Протягом перших 24 годин після публікації контент зазвичай збирає 1.36% реакцій від загальної кількості підписників.
- Охоплення публікацій: В середньому кожен допис отримує 2 744 переглядів. Протягом першої доби публікація в середньому набирає 1 026 переглядів.
- Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 5.
- 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 13 червня, 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.
75 676
Підписники
+3124 години
+2057 днів
+92330 день
Архів дописів
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✅ Data Visualization with Matplotlib 📊
🛠 Tools:
⦁
matplotlib.pyplot – Basic plots
⦁ seaborn – Cleaner, statistical plots
1️⃣ Line Chart – to show trends over time
import matplotlib.pyplot as plt
days = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri']
sales = [200, 450, 300, 500, 650]
plt.plot(days, sales, marker='o')
plt.title('Daily Sales')
plt.xlabel('Day')
plt.ylabel('Sales')
plt.grid(True)
plt.show()
products = ['A', 'B', 'C', 'D']
revenue = [1000, 1500, 700, 1200]
plt.bar(products, revenue, color='skyblue')
plt.title('Revenue by Product')
plt.xlabel('Product')
plt.ylabel('Revenue')
plt.show()
labels = ['iOS', 'Android', 'Others']
market_share = [40, 55, 5]
plt.pie(market_share, labels=labels, autopct='%1.1f%%', startangle=140)
plt.title('Mobile OS Market Share')
plt.axis('equal') # perfect circle
plt.show()
ages = [22, 25, 27, 30, 32, 35, 35, 40, 45, 50, 52, 60]
plt.hist(ages, bins=5, color='green', edgecolor='black')
plt.title('Age Distribution')
plt.xlabel('Age Groups')
plt.ylabel('Frequency')
plt.show()
income = [30, 35, 40, 45, 50, 55, 60]
spending = [20, 25, 30, 32, 35, 40, 42]
plt.scatter(income, spending, color='red')
plt.title('Income vs Spending')
plt.xlabel('Income (k)')
plt.ylabel('Spending (k)')
plt.show()
import seaborn as sns
import pandas as pd
data = {'Math': [90, 80, 85, 95],
'Science': [85, 89, 92, 88],
'English': [78, 75, 80, 85]}
df = pd.DataFrame(data)
corr = df.corr()
sns.heatmap(corr, annot=True, cmap='coolwarm')
plt.title('Subject Score Correlation')
plt.show()
Commonly used Power BI DAX functions:
DATE AND TIME FUNCTIONS:
-
CALENDAR
- DATEDIFF
- TODAY, DAY, MONTH, QUARTER, YEAR
AGGREGATE FUNCTIONS:
- SUM, SUMX, PRODUCT
- AVERAGE
- MIN, MAX
- COUNT
- COUNTROWS
- COUNTBLANK
- DISTINCTCOUNT
FILTER FUNCTIONS:
- CALCULATE
- FILTER
- ALL, ALLEXCEPT, ALLSELECTED, REMOVEFILTERS
- SELECTEDVALUE
TIME INTELLIGENCE FUNCTIONS:
- DATESBETWEEN
- DATESMTD, DATESQTD, DATESYTD
- SAMEPERIODLASTYEAR
- PARALLELPERIOD
- TOTALMTD, TOTALQTD, TOTALYTD
TEXT FUNCTIONS:
- CONCATENATE
- FORMAT
- LEN, LEFT, RIGHT
INFORMATION FUNCTIONS:
- HASONEVALUE, HASONEFILTER
- ISBLANK, ISERROR, ISEMPTY
- CONTAINS
LOGICAL FUNCTIONS:
- AND, OR, IF, NOT
- TRUE, FALSE
- SWITCH
RELATIONSHIP FUNCTIONS:
- RELATED
- USERRELATIONSHIP
- RELATEDTABLE
Remember, DAX is more about logic than the formulas.
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Python for Data Science: NumPy & Pandas 📊🐍
🧮 Step 1: Learn NumPy (for numbers and arrays)
What is NumPy?
A fast Python library for working with numbers and arrays.
➤ 1. What is an array?
Like a list of numbers:
[1, 2, 3, 4]
import numpy as np
a = np.array([1, 2, 3, 4])
a * 2 # array([2, 4, 6, 8])
a.mean() # average
np.max(a) # max number
np.min(a) # min number
a[0:2] # slicing → [1, 2]
a + b, a * 2
⦁ Slicing and indexing: a[0:2], a[:,1]
⦁ Broadcasting: operations on arrays with different shapes
⦁ Useful functions: np.mean(), np.std(), np.linspace(), np.random.randn()
————————
📊 Step 2: Learn Pandas (for tables like Excel)
What is Pandas?
Python tool to read, clean & analyze data — like Excel but supercharged.
➤ 1. What’s a DataFrame?
Like an Excel sheet, rows & columns.
import pandas as pd
df = pd.read_csv("sales.csv")
df.head() # first 5 rows
df.info() # rows, columns, missing data
df.describe() # stats like mean, min, max
df['product']
df[df['price'] > 100]
df.groupby('category')['price'].mean()
merged = pd.merge(df1, df2, on='customer_id')
df.isnull() # where missing
df.dropna() # drop missing rows
df.fillna(0) # fill missing with 0
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🗓️ Python Basics You Should Know 🐍
✅ 1. Variables & Data Types
Variables store data. Data types show what kind of data it is.
# String (text)
name = "Alice"
# Integer (whole number)
age = 25
# Float (decimal)
height = 5.6
# Boolean (True/False)
is_student = True
🔹 Use type() to check data type:
print(type(name)) # <class 'str'>
✅ 2. Lists and Tuples
⦁ List = changeable collection
fruits = ["apple", "banana", "cherry"]
print(fruits) # banana
fruits.append("orange") # add item
⦁ Tuple = fixed collection (cannot change items)
colors = ("red", "green", "blue")
print(colors) # red
✅ 3. Dictionaries
Store data as key-value pairs.
person = {
"name": "John",
"age": 22,
"city": "Seoul"
}
print(person["name"]) # John
✅ 4. Conditional Statements (if-else)
Make decisions.
age = 20
if age >= 18:
print("Adult")
else:
print("Minor")
🔹 Use elif for multiple conditions:
if age < 13:
print("Child")
elif age < 18:
print("Teenager")
else:
print("Adult")
✅ 5. Loops
Repeat code.
⦁ For Loop – fixed repeats
for i in range(3):
print("Hello", i)
⦁ While Loop – repeats while true
count = 1
while count <= 3:
print("Count is", count)
count += 1
✅ 6. Functions
Reusable code blocks.
def greet(name):
print("Hello", name)
greet("Alice") # Hello Alice
🔹 Return result:
def add(a, b):
return a + b
print(add(3, 5)) # 8
✅ 7. Input / Output
Get user input and show messages.
name = input("Enter your name: ")
print("Hi", name)
🧪 Mini Projects
1. Number Guessing Game
import random
num = random.randint(1, 10)
guess = int(input("Guess a number (1-10): "))
if guess == num:
print("Correct!")
else:
print("Wrong, number was", num)
2. To-Do List
todo = []
todo.append("Buy milk")
todo.append("Study Python")
print(todo)
🛠️ Recommended Tools
⦁ Google Colab (online)
⦁ Jupyter Notebook
⦁ Python IDLE or VS Code
💡 Tips
Practice a bit daily, start simple, and focus on basics — they matter most!
Double Tap ♥️ For More
✅ 8-Week Beginner Roadmap to Learn Data Science 📊🚀
🗓️ Week 1: Python Basics
Goal: Understand basic Python syntax & data types
Topics: Variables, lists, dictionaries, loops, functions
Tools: Jupyter Notebook / Google Colab
Mini Project: Calculator or number guessing game
🗓️ Week 2: Python for Data
Goal: Learn data manipulation with NumPy & Pandas
Topics: Arrays, DataFrames, filtering, groupby, joins
Tools: Pandas, NumPy
Mini Project: Analyze a CSV (e.g., sales or weather data)
🗓️ Week 3: Data Visualization
Goal: Visualize data trends & patterns
Topics: Line, bar, scatter, histograms, heatmaps
Tools: Matplotlib, Seaborn
Mini Project: Visualize COVID or stock market data
🗓️ Week 4: Statistics & Probability Basics
Goal: Understand core statistical concepts
Topics: Mean, median, mode, std dev, probability, distributions
Tools: Python, SciPy
Mini Project: Analyze survey data & generate insights
🗓️ Week 5: Exploratory Data Analysis (EDA)
Goal: Draw insights from real datasets
Topics: Data cleaning, outliers, correlation
Tools: Pandas, Seaborn
Mini Project: EDA on Titanic or Iris dataset
🗓️ Week 6: Intro to Machine Learning
Goal: Learn ML workflow & basic algorithms
Topics: Supervised vs unsupervised, train/test split
Tools: Scikit-learn
Mini Project: Predict house prices (Linear Regression)
🗓️ Week 7: Classification Models
Goal: Understand and apply classification
Topics: Logistic Regression, KNN, Decision Trees
Tools: Scikit-learn
Mini Project: Titanic survival prediction
🗓️ Week 8: Capstone Project + Deployment
Goal: Apply all concepts in one end-to-end project
Ideas: Sales prediction, Movie rating analysis, Customer churn detection
Tools: Streamlit (for simple web app)
Bonus: Upload your project on GitHub
💡 Tips:
⦁ Practice daily on platforms like Kaggle or Google Colab
⦁ Join beginner projects on GitHub
⦁ Share progress on LinkedIn or X (Twitter)
💬 Tap ❤️ for the detailed explanation of each topic!
Data Scientist Roadmap 📈
📂 Python Basics
∟📂 Numpy & Pandas
∟📂 Data Cleaning
∟📂 Data Visualization (Seaborn, Plotly)
∟📂 Statistics & Probability
∟📂 Machine Learning (Sklearn)
∟📂 Deep Learning (TensorFlow / PyTorch)
∟📂 Model Deployment
∟📂 Real-World Projects
∟✅ Apply for Data Science Roles
React "❤️" For More
ML interview Question 📚
What is Quantization in machine learning?
Quantization the process of reducing the precision of the numbers used to represent a model's parameters, such as weights and activations. This is often done by converting 32-bit floating-point numbers (commonly used in training) to lower precision formats, like 16-bit or 8-bit integers.
Quantization is primarily used during model inference to:
1. Reduce model size: Lower precision numbers require less memory.
2. Improve computational efficiency: Operations on lower-precision data types are faster and require less power.
3. Speed up inference: Smaller models can be loaded faster, improving performance on edge devices like smartphones or IoT devices.
Quantization can lead to a small loss in model accuracy, as reducing precision can introduce rounding errors. But in many cases, the trade-off between accuracy and efficiency is worthwhile, especially for deployment on resource-constrained devices.
There are different types of quantization:
1. Post-training quantization: Applied after the model has been trained.
2.Quantization-aware training (QAT): Takes quantization into account during the training process to minimize the accuracy drop.
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Essential Topics to Master Data Science Interviews: 🚀
SQL:
1. Foundations
- Craft SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
- Embrace Basic JOINS (INNER, LEFT, RIGHT, FULL)
- Navigate through simple databases and tables
2. Intermediate SQL
- Utilize Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
- Embrace Subqueries and nested queries
- Master Common Table Expressions (WITH clause)
- Implement CASE statements for logical queries
3. Advanced SQL
- Explore Advanced JOIN techniques (self-join, non-equi join)
- Dive into Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)
- Optimize queries with indexing
- Execute Data manipulation (INSERT, UPDATE, DELETE)
Python:
1. Python Basics
- Grasp Syntax, variables, and data types
- Command Control structures (if-else, for and while loops)
- Understand Basic data structures (lists, dictionaries, sets, tuples)
- Master Functions, lambda functions, and error handling (try-except)
- Explore Modules and packages
2. Pandas & Numpy
- Create and manipulate DataFrames and Series
- Perfect Indexing, selecting, and filtering data
- Handle missing data (fillna, dropna)
- Aggregate data with groupby, summarizing data
- Merge, join, and concatenate datasets
3. Data Visualization with Python
- Plot with Matplotlib (line plots, bar plots, histograms)
- Visualize with Seaborn (scatter plots, box plots, pair plots)
- Customize plots (sizes, labels, legends, color palettes)
- Introduction to interactive visualizations (e.g., Plotly)
Excel:
1. Excel Essentials
- Conduct Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
- Dive into charts and basic data visualization
- Sort and filter data, use Conditional formatting
2. Intermediate Excel
- Master Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
- Leverage PivotTables and PivotCharts for summarizing data
- Utilize data validation tools
- Employ What-if analysis tools (Data Tables, Goal Seek)
3. Advanced Excel
- Harness Array formulas and advanced functions
- Dive into Data Model & Power Pivot
- Explore Advanced Filter, Slicers, and Timelines in Pivot Tables
- Create dynamic charts and interactive dashboards
Power BI:
1. Data Modeling in Power BI
- Import data from various sources
- Establish and manage relationships between datasets
- Grasp Data modeling basics (star schema, snowflake schema)
2. Data Transformation in Power BI
- Use Power Query for data cleaning and transformation
- Apply advanced data shaping techniques
- Create Calculated columns and measures using DAX
3. Data Visualization and Reporting in Power BI
- Craft interactive reports and dashboards
- Utilize Visualizations (bar, line, pie charts, maps)
- Publish and share reports, schedule data refreshes
Statistics Fundamentals:
- Mean, Median, Mode
- Standard Deviation, Variance
- Probability Distributions, Hypothesis Testing
- P-values, Confidence Intervals
- Correlation, Simple Linear Regression
- Normal Distribution, Binomial Distribution, Poisson Distribution.
Show some ❤️ if you're ready to elevate your data science game! 📊
ENJOY LEARNING 👍👍
Where Each Programming Language Shines 🚀👨🏻💻
❯ C ➟ OS Development, Embedded Systems, Game Engines
❯ C++ ➟ Game Development, High-Performance Applications, Financial Systems
❯ Java ➟ Enterprise Software, Android Development, Backend Systems
❯ C# ➟ Game Development (Unity), Windows Applications, Enterprise Software
❯ Python ➟ AI/ML, Data Science, Web Development, Automation
❯ JavaScript ➟ Frontend Web Development, Full-Stack Apps, Game Development
❯ Golang ➟ Cloud Services, Networking, High-Performance APIs
❯ Swift ➟ iOS/macOS App Development
❯ Kotlin ➟ Android Development, Backend Services
❯ PHP ➟ Web Development (WordPress, Laravel)
❯ Ruby ➟ Web Development (Ruby on Rails), Prototyping
❯ Rust ➟ Systems Programming, High-Performance Computing, Blockchain
❯ Lua ➟ Game Scripting (Roblox, WoW), Embedded Systems
❯ R ➟ Data Science, Statistics, Bioinformatics
❯ SQL ➟ Database Management, Data Analytics
❯ TypeScript ➟ Scalable Web Applications, Large JavaScript Projects
❯ Node.js ➟ Backend Development, Real-Time Applications
❯ React ➟ Modern Web Applications, Interactive UIs
❯ Vue ➟ Lightweight Frontend Development, SPAs
❯ Django ➟ Scalable Web Applications, AI/ML Backend
❯ Laravel ➟ Full-Stack PHP Development
❯ Blazor ➟ Web Apps with .NET
❯ Spring Boot ➟ Enterprise Java Applications, Microservices
❯ Ruby on Rails ➟ Startup Web Apps, MVP Development
❯ HTML/CSS ➟ Web Design, UI Development
❯ GIT ➟ Version Control, Collaboration
❯ Linux ➟ Server Management, Security, DevOps
❯ DevOps ➟ Infrastructure Automation, CI/CD
❯ CI/CD ➟ Continuous Deployment & Testing
❯ Docker ➟ Containerization, Cloud Deployments
❯ Kubernetes ➟ Scalable Cloud Orchestration
❯ Microservices ➟ Distributed Systems, Scalable Backends
❯ Selenium ➟ Web Automation Testing
❯ Playwright ➟ Modern Browser Automation
React ❤️ for more
Python Learning Plan in 2025
|-- Week 1: Introduction to Python
| |-- Python Basics
| | |-- What is Python?
| | |-- Installing Python
| | |-- Introduction to IDEs (Jupyter, VS Code)
| |-- Setting up Python Environment
| | |-- Anaconda Setup
| | |-- Virtual Environments
| | |-- Basic Syntax and Data Types
| |-- First Python Program
| | |-- Writing and Running Python Scripts
| | |-- Basic Input/Output
| | |-- Simple Calculations
|
|-- Week 2: Core Python Concepts
| |-- Control Structures
| | |-- Conditional Statements (if, elif, else)
| | |-- Loops (for, while)
| | |-- Comprehensions
| |-- Functions
| | |-- Defining Functions
| | |-- Function Arguments and Return Values
| | |-- Lambda Functions
| |-- Modules and Packages
| | |-- Importing Modules
| | |-- Standard Library Overview
| | |-- Creating and Using Packages
|
|-- Week 3: Advanced Python Concepts
| |-- Data Structures
| | |-- Lists, Tuples, and Sets
| | |-- Dictionaries
| | |-- Collections Module
| |-- File Handling
| | |-- Reading and Writing Files
| | |-- Working with CSV and JSON
| | |-- Context Managers
| |-- Error Handling
| | |-- Exceptions
| | |-- Try, Except, Finally
| | |-- Custom Exceptions
|
|-- Week 4: Object-Oriented Programming
| |-- OOP Basics
| | |-- Classes and Objects
| | |-- Attributes and Methods
| | |-- Inheritance
| |-- Advanced OOP
| | |-- Polymorphism
| | |-- Encapsulation
| | |-- Magic Methods and Operator Overloading
| |-- Design Patterns
| | |-- Singleton
| | |-- Factory
| | |-- Observer
|
|-- Week 5: Python for Data Analysis
| |-- NumPy
| | |-- Arrays and Vectorization
| | |-- Indexing and Slicing
| | |-- Mathematical Operations
| |-- Pandas
| | |-- DataFrames and Series
| | |-- Data Cleaning and Manipulation
| | |-- Merging and Joining Data
| |-- Matplotlib and Seaborn
| | |-- Basic Plotting
| | |-- Advanced Visualizations
| | |-- Customizing Plots
|
|-- Week 6-8: Specialized Python Libraries
| |-- Web Development
| | |-- Flask Basics
| | |-- Django Basics
| |-- Data Science and Machine Learning
| | |-- Scikit-Learn
| | |-- TensorFlow and Keras
| |-- Automation and Scripting
| | |-- Automating Tasks with Python
| | |-- Web Scraping with BeautifulSoup and Scrapy
| |-- APIs and RESTful Services
| | |-- Working with REST APIs
| | |-- Building APIs with Flask/Django
|
|-- Week 9-11: Real-world Applications and Projects
| |-- Capstone Project
| | |-- Project Planning
| | |-- Data Collection and Preparation
| | |-- Building and Optimizing Models
| | |-- Creating and Publishing Reports
| |-- Case Studies
| | |-- Business Use Cases
| | |-- Industry-specific Solutions
| |-- Integration with Other Tools
| | |-- Python and SQL
| | |-- Python and Excel
| | |-- Python and Power BI
|
|-- Week 12: Post-Project Learning
| |-- Python for Automation
| | |-- Automating Daily Tasks
| | |-- Scripting with Python
| |-- Advanced Python Topics
| | |-- Asyncio and Concurrency
| | |-- Advanced Data Structures
| |-- Continuing Education
| | |-- Advanced Python Techniques
| | |-- Community and Forums
| | |-- Keeping Up with Updates
|
|-- Resources and Community
| |-- Online Courses (Coursera, edX, Udemy)
| |-- Books (Automate the Boring Stuff, Python Crash Course)
| |-- Python Blogs and Podcasts
| |-- GitHub Repositories
| |-- Python Communities (Reddit, Stack Overflow)
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