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Data Analytics
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Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making. Admin: @HusseinSheikho || @Hussein_Sheikho
Show more📈 Analytical overview of Telegram channel Data Analytics
Channel Data Analytics (@dataanalyticsx) in the English language segment is an active participant. Currently, the community unites 28 920 subscribers, ranking 4 741 in the Technologies & Applications category and 22 829 in the Russia region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 28 920 subscribers.
According to the latest data from 10 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 490 over the last 30 days and by 16 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 4.41%. Within the first 24 hours after publication, content typically collects 1.27% reactions from the total number of subscribers.
- Post reach: On average, each post receives 1 275 views. Within the first day, a publication typically gains 368 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 2.
- Thematic interests: Content is focused on key topics such as sellerflash, buybox, buyer, chaos, effortless.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Thanks to the high frequency of updates (latest data received on 11 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.
28 920
Subscribers
+1624 hours
+677 days
+49030 days
Posts Archive
28 920
Repost from Learn Python Coding
This channels is for Programmers, Coders, Software Engineers.
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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
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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!...
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28 920
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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
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28 920
Repost from Machine Learning with Python
Free access to over 40 courses
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28 920
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28 920
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28 920
Repost from Machine Learning with Python
🎯 2026 IT Certification Prep Kit – Free!
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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.
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https://t.me/CodeProgrammer
