متاح الآن! بحث تيليغرام 2025 — أهم رؤى العام 
Data Analytics
الذهاب إلى القناة على Telegram
Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making. Admin: @HusseinSheikho || @Hussein_Sheikho
إظهار المزيد📈 نظرة تحليلية على قناة تيليجرام Data Analytics
تُعد قناة Data Analytics (@dataanalyticsx) في القطاع اللغوي الإنكليزية لاعباً نشطاً. يضم المجتمع حالياً 28 920 مشتركاً، محتلاً المرتبة 4 741 في فئة التكنولوجيات والتطبيقات والمرتبة 22 829 في منطقة روسيا.
📊 مؤشرات الجمهور والحراك
منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 28 920 مشتركاً.
بحسب آخر البيانات بتاريخ 10 يونيو, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار 490، وفي آخر 24 ساعة بمقدار 16، مع بقاء الوصول العام مرتفعاً.
- حالة التحقق: غير موثّقة
- معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 4.41%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 1.27% من ردود الفعل نسبةً إلى إجمالي المشتركين.
- وصول المنشورات: يحصل كل منشور على متوسط 1 275 مشاهدة. وخلال اليوم الأول يجمع عادةً 368 مشاهدة.
- التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 2.
- الاهتمامات الموضوعية: يركز المحتوى على مواضيع رئيسية مثل sellerflash, buybox, buyer, chaos, effortless.
📝 الوصف وسياسة المحتوى
يصف المؤلف القناة بأنها مساحة للتعبير عن الآراء الذاتية:
“Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.
Admin: @HusseinSheikho || @Hussein_Sheikho”
بفضل وتيرة التحديث المرتفعة (أحدث البيانات بتاريخ 11 يونيو, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التكنولوجيات والتطبيقات.
28 920
المشتركون
+1624 ساعات
+677 أيام
+49030 أيام
أرشيف المشاركات
28 920
Repost from Learn Python Coding
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
✅ https://t.me/addlist/8_rRW2scgfRhOTc0
✅ https://t.me/Codeprogrammer
28 920
28 920
SQL Cheat Sheet for Interview 2026
Master #SQL with this cheat sheet, covering querying, commands, filtering, aggregation and basics to advance. Perfect for coding interviews and tech job prep
Read: https://www.almabetter.com/bytes/cheat-sheet/sql
28 920
Repost from Machine Learning with Python
This cheat sheet—part of our Complete Guide to #NumPy, #pandas, and #DataVisualization—offers a handy reference for essential pandas commands, focused on efficient #datamanipulation and analysis. Using examples from the Fortune 500 Companies #Dataset, it covers key pandas operations such as reading and writing data, selecting and filtering DataFrame values, and performing common transformations.
You'll find easy-to-follow examples for grouping, sorting, and aggregating data, as well as calculating statistics like mean, correlation, and summary statistics. Whether you're cleaning datasets, analyzing trends, or visualizing data, this cheat sheet provides concise instructions to help you navigate pandas’ powerful functionality.
Designed to be practical and actionable, this guide ensures you can quickly apply pandas’ versatile data manipulation tools in your workflow.
https://t.me/CodeProgrammer
28 920
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
✅ https://t.me/addlist/8_rRW2scgfRhOTc0
✅ https://t.me/Codeprogrammer
28 920
Repost from Machine Learning with Python
Pandas vs. Polars: A Complete Comparison of Syntax, Speed, and Memory
Need help choosing the right #Python dataframe library? This article compares #Pandas and #Polars to help you decide.
If you've been working with data in Python, you've almost certainly used pandas. It's been the go-to library for data manipulation for over a decade. But recently, Polars has been gaining serious traction. Polars promises to be faster, more memory-efficient, and more intuitive than pandas. But is it worth learning? And how different is it really?
In this article, we'll compare pandas and Polars side-by-side. You'll see performance benchmarks, and learn the syntax differences. By the end, you'll be able to make an informed decision for your next data project.
Read: https://www.kdnuggets.com/pandas-vs-polars-a-complete-comparison-of-syntax-speed-and-memory
https://t.me/CodeProgrammer 🌺
28 920
Track Expenses in Telegram — 2 Seconds
Coinka
Voice input, receipt photos, AI categories. Multi-currency support, budget limits, shared family budgets. No extra apps — everything inside Telegram.
Ad. 18+
28 920
Repost from Machine Learning with Python
Over 20 free courses are now available on our channel for a very limited time.
https://t.me/DataScienceC
28 920
📊 5 Useful Python Scripts for Automated Data Quality Checks
📌 Introduction
Data quality issues are pervasive and can lead to incorrect business decisions, broken analysis, and pipeline failures. Manual data validation is time-consuming and prone to errors, making it essential to automate the process. This article discusses five useful Python scripts for automated data quality checks, addressing common issues such as missing data, invalid data types, duplicate records, outliers, and cross-field inconsistencies.
📌 Main Content / Discussion
The five Python scripts are designed to handle specific data quality issues.
import pandas as pd
import numpy as np
# Example 1: Missing data analyzer script
def analyze_missing_data(df):
missing_data = df.isnull().sum()
return missing_data
# Example 2: Data type validator script
def validate_data_types(df, schema):
for column, dtype in schema.items():
if df[column].dtype != dtype:
print(f"Invalid data type for column {column}")
return df
# Example 3: Duplicate record detector script
def detect_duplicates(df):
duplicates = df.duplicated().sum()
return duplicates
# Example 4: Outlier detection script
def detect_outliers(df, column):
Q1 = df[column].quantile(0.25)
Q3 = df[column].quantile(0.75)
IQR = Q3 - Q1
lower_bound = Q1 - 1.5 * IQR
upper_bound = Q3 + 1.5 * IQR
outliers = df[(df[column] < lower_bound) | (df[column] > upper_bound)]
return outliers
# Example 5: Cross-field consistency checker script
def check_cross_field_consistency(df):
# Check for temporal consistency
df['start_date'] = pd.to_datetime(df['start_date'])
df['end_date'] = pd.to_datetime(df['end_date'])
inconsistencies = df[df['start_date'] > df['end_date']]
return inconsistencies
28 920
Repost from Udemy Free Coupons
Data Science NumPy & Pandas - Practice Questions 2026
Data Science NumPy & Pandas 120 unique high-quality test questions with detailed explanations!...
🏷 Category: it-and-software
🌍 Language: English (US)
👥 Students: 24 students
⭐️ Rating: 0.0/5.0 (0 reviews)
🏃♂️ Enrollments Left: 63
⏳ Expires In: 0D:30H:30M
💰 Price: $1 => FREE
🆔 Coupon: 3EC4DB90A260F1AC7B39
⚠️ Please note: A verification layer has been added to prevent bad actors and bots from claiming the courses, so it is important for genuine users to enroll manually to not lose this free opportunity.
💎 By: https://t.me/DataScienceC
28 920
Ору, на 1win можно поставить на бан телеги до апреля с кэфом 4.7
Кто еще не регался, ставьте промокод DUROVVV для бонуса к депозиту +500%
28 920
#Polars Cheat Sheet with example
Learn and Run online: https://colab.research.google.com/github/FranzDiebold/polars-cheat-sheet/blob/main/polars-cheat-sheet.ipynb
https://t.me/DataAnalyticsX 🔴
28 920
Cheatsheet for Pandas to Polar
Getting started with Polars? This post shows you how to convert some familar Pandas commands to #Polars. But it also tries to go beyond that to introduce you to some of the more fundamental differences between Pandas and Polars.
https://www.rhosignal.com/posts/polars-pandas-cheatsheet/
28 920
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
✅ https://t.me/addlist/8_rRW2scgfRhOTc0
✅ https://t.me/Codeprogrammer
28 920
Pepe VPN - быстрый, безопасный VPN
Pepe VPN
Бесплатный пробный доступ
Обход любых блокировок
Работает на любых устройствах
Ad. 18+
28 920
Repost from Machine Learning with Python
Free access to over 40 courses
https://lve.to/jwxfnss0yi
28 920
Pepe VPN - быстрый, безопасный VPN
Pepe VPN
Бесплатный пробный доступ
Обход любых блокировок
Работает на любых устройствах
Ad. 18+
28 920
Pepe VPN - быстрый, безопасный VPN
Pepe VPN
Бесплатный пробный доступ
Обход любых блокировок
Работает на любых устройствах
Ad. 18+
28 920
Repost from Machine Learning with Python
🎯 2026 IT Certification Prep Kit – Free!
🔥Whether you're preparing for #Python, #Cisco, #PMI, #Fortinet, #AWS, #Azure, #AI, #Excel, #comptia, #ITIL, #cloud or any other in-demand certification – SPOTO has got you covered!
✅ What’s Inside:
・Free Python, Excel, Cyber Security, Cisco, SQL, ITIL, PMP, AWS courses: https://bit.ly/3M9h5su
・IT Certs E-book: https://bit.ly/3Mlu5ez
・IT Exams Skill Test: https://bit.ly/3NVrgRU
・Free Cloud Study Guide: https://bit.ly/4kgFVDs
・Free AI material and support tools：https://bit.ly/46qvpDX
👉 Become Part of Our IT Learning Circle! resources and support:
https://chat.whatsapp.com/FlG2rOYVySLEHLKXF3nKGB
💬 Want exam help? Chat with an admin now!
wa.link/8fy3x4
28 920
Repost from Machine Learning with Python
🏷 Comprehensive Data Science Roadmap Notes
✅ This roadmap is exactly the secret recipe you need to get out of confusion and know how to step-by-step prepare yourself for the job market.
🕡 From mastering Python and SQL to cleaning data and working with cloud tools, which are prerequisites for any project.
🕑 How to extract real analysis reports and strategies from raw data using statistics and visualization tools.
🕗 You will learn everything from machine learning and advanced algorithms to precise model evaluation.
🕙 Get familiar with neural networks, generative artificial intelligence, and language models to have a voice in today's modern world.
🕧 How to build real projects and portfolios that are exactly what hiring managers and big companies are looking for.
🌐 #DataScience #DataScience #pytorch #python #Roadmap
https://t.me/CodeProgrammer
