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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
Mostrar más📈 Análisis del canal de Telegram Data Analytics
El canal Data Analytics (@dataanalyticsx) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 28 920 suscriptores, ocupando la posición 4 741 en la categoría Tecnologías y Aplicaciones y el puesto 22 829 en la región Rusia.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 28 920 suscriptores.
Según los últimos datos del 10 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 490, y en las últimas 24 horas de 16, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 4.41%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.27% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 1 275 visualizaciones. En el primer día suele acumular 368 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 2.
- Intereses temáticos: El contenido se centra en temas clave como sellerflash, buybox, buyer, chaos, effortless.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 11 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Tecnologías y Aplicaciones.
28 920
Suscriptores
+1624 horas
+677 días
+49030 días
Archivo de publicaciones
28 920
Repost from Learn Python Coding
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28 920
28 920
SQL Cheat Sheet for Interview 2026
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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.
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1️⃣ Data Science
2️⃣ Machine Learning
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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
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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
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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
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28 920
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Repost from Machine Learning with Python
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28 920
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Repost from Machine Learning with Python
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28 920
Repost from Machine Learning with Python
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