Available now! Telegram Research 2025 โ the year's key insights 
Python for Data Analysts
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Find top Python resources from global universities, cool projects, and learning materials for data analytics. For promotions: @coderfun Useful links: heylink.me/DataAnalytics
Show more๐ Analytical overview of Telegram channel Python for Data Analysts
Channel Python for Data Analysts (@pythonanalyst) in the English language segment is an active participant. Currently, the community unites 51 505 subscribers, ranking 2 607 in the Technologies & Applications category and 7 392 in the India region.
๐ Audience metrics and dynamics
Since its creation on ะฝะตะฒัะดะพะผะพ, the project has demonstrated rapid growth, gathering an audience of 51 505 subscribers.
According to the latest data from 05 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 255 over the last 30 days and by 22 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 4.29%. Within the first 24 hours after publication, content typically collects N/A% reactions from the total number of subscribers.
- Post reach: On average, each post receives 2 209 views. Within the first day, a publication typically gains 0 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 8.
- Thematic interests: Content is focused on key topics such as visualization, panda, analyst, sql, analytic.
๐ Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
โFind top Python resources from global universities, cool projects, and learning materials for data analytics.
For promotions: @coderfun
Useful links: heylink.me/DataAnalyticsโ
Thanks to the high frequency of updates (latest data received on 07 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.
51 505
Subscribers
+2224 hours
+627 days
+25530 days
Posts Archive
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Data analytics is not about the the tools you master but about the people you influence.
I see many debates around the best tools such as:
- Excel vs SQL
- Python vs R
- Tableau vs PowerBI
- ChatGPT vs no ChatGPT
The truth is that business doesn't care about how you come up with your insights.
All business cares about is:
- the story line
- how well they can understand it
- your communication style
- the overall feeling after a presentation
These make the difference in being perceived as a great data analyst...
not the tools you may or may not master ๐
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โ Data Structures โ Lists, Tuples, Sets, Dictionaries
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โ Object-Oriented Programming (OOP) โ Classes, Objects, Inheritance, Polymorphism
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Essential Python Libraries for Data Science
- Numpy: Fundamental for numerical operations, handling arrays, and mathematical functions.
- SciPy: Complements Numpy with additional functionalities for scientific computing, including optimization and signal processing.
- Pandas: Essential for data manipulation and analysis, offering powerful data structures like DataFrames.
- Matplotlib: A versatile plotting library for creating static, interactive, and animated visualizations.
- Keras: A high-level neural networks API, facilitating rapid prototyping and experimentation in deep learning.
- TensorFlow: An open-source machine learning framework widely used for building and training deep learning models.
- Scikit-learn: Provides simple and efficient tools for data mining, machine learning, and statistical modeling.
- Seaborn: Built on Matplotlib, Seaborn enhances data visualization with a high-level interface for drawing attractive and informative statistical graphics.
- Statsmodels: Focuses on estimating and testing statistical models, providing tools for exploring data, estimating models, and statistical testing.
- NLTK (Natural Language Toolkit): A library for working with human language data, supporting tasks like classification, tokenization, stemming, tagging, parsing, and more.
These libraries collectively empower data scientists to handle various tasks, from data preprocessing to advanced machine learning implementations.
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๐๐ผ๐ผ๐ด๐น๐ฒ ๐๐ฅ๐๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฒ๐ฑ ๐๐ผ๐๐ฟ๐๐ฒ๐ - ๐ ๐ฎ๐๐๐ฒ๐ฟ ๐๐ถ๐ด๐ต ๐๐ฒ๐บ๐ฎ๐ป๐ฑ ๐ง๐ฒ๐ฐ๐ต ๐ฆ๐ธ๐ถ๐น๐น๐ ๐
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How to get job as python fresher?
1. Get Your Python Fundamentals Strong
You should have a clear understanding of Python syntax, statements, variables & operators, control structures, functions & modules, OOP concepts, exception handling, and various other concepts before going out for a Python interview.
2. Learn Python Frameworks
As a beginner, youโre recommended to start with Django as it is considered the standard framework for Python by many developers. An adequate amount of experience with frameworks will not only help you to dive deeper into the Python world but will also help you to stand out among other Python freshers.
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You can start it by building several minor projects such as Number guessing game, Hangman Game, Website Blocker, and many others. Also, you can opt to build few advanced-level projects once youโll learn several Python web frameworks and other trending technologies.
@crackingthecodinginterview
4. Get Exposure to Trending Technologies Using Python.
Python is being used with almost every latest tech trend whether it be Artificial Intelligence, Internet of Things (IOT), Cloud Computing, or any other. And getting exposure to these upcoming technologies using Python will not only make you industry-ready but will also give you an edge over others during a career opportunity.
5. Do an Internship & Grow Your Network.
You need to connect with those professionals who are already working in the same industry in which you are aspiring to get into such as Data Science, Machine learning, Web Development, etc.
Python Interview Q&A: https://topmate.io/coding/898340
Like for more โค๏ธ
ENJOY LEARNING ๐๐
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๐๐ฟ๐ฒ๐ฒ ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐๐ผ ๐๐ถ๐ฐ๐ธ๐๐๐ฎ๐ฟ๐ ๐ฌ๐ผ๐๐ฟ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐๐ผ๐๐ฟ๐ป๐ฒ๐ ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฑ๐
Ready to upskill in data science for free?๐
Here are 3 amazing courses to build a strong foundation in Exploratory Data Analysis, SQL, and Python๐จโ๐ป๐
๐๐ข๐ง๐ค๐:-
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Take the first step towards your dream career!โ
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๐ฑ ๐ฃ๐ผ๐๐ฒ๐ฟ๐ณ๐๐น ๐ฃ๐๐๐ต๐ผ๐ป ๐ฃ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐๐ ๐๐ผ ๐๐ฑ๐ฑ ๐๐ผ ๐ฌ๐ผ๐๐ฟ ๐ฅ๐ฒ๐๐๐บ๐ฒ ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฑ๐
Looking to land an internship, secure a tech job, or start freelancing in 2025?๐จโ๐ป
Python projects are one of the best ways to showcase your skills and stand out in todayโs competitive job market๐ฃ๐
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Stand out in todayโs competitive job marketโ
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๐๐ฆ๐ฉ๐จ๐ซ๐ญ๐ข๐ง๐ ๐๐๐๐๐ฌ๐ฌ๐๐ซ๐ฒ ๐๐ข๐๐ซ๐๐ซ๐ข๐๐ฌ:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
๐๐จ๐๐๐ข๐ง๐ ๐ญ๐ก๐ ๐๐๐ญ๐๐ฌ๐๐ญ:
df = pd.read_csv('your_dataset.csv')
๐๐ง๐ข๐ญ๐ข๐๐ฅ ๐๐๐ญ๐ ๐๐ง๐ฌ๐ฉ๐๐๐ญ๐ข๐จ๐ง:
1- View the first few rows:
df.head()
2- Summary of the dataset:
df.info()
3- Statistical summary:
df.describe()
๐๐๐ง๐๐ฅ๐ข๐ง๐ ๐๐ข๐ฌ๐ฌ๐ข๐ง๐ ๐๐๐ฅ๐ฎ๐๐ฌ:
1- Identify missing values:
df.isnull().sum()
2- Visualize missing values:
sns.heatmap(df.isnull(), cbar=False, cmap='viridis')
plt.show()
๐๐๐ญ๐ ๐๐ข๐ฌ๐ฎ๐๐ฅ๐ข๐ณ๐๐ญ๐ข๐จ๐ง:
1- Histograms:
df.hist(bins=30, figsize=(20, 15))
plt.show()
2 - Box plots:
plt.figure(figsize=(10, 6))
sns.boxplot(data=df)
plt.xticks(rotation=90)
plt.show()
3- Pair plots:
sns.pairplot(df)
plt.show()
4- Correlation matrix and heatmap:
correlation_matrix = df.corr()
plt.figure(figsize=(12, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.show()
๐๐๐ญ๐๐ ๐จ๐ซ๐ข๐๐๐ฅ ๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ฌ๐ข๐ฌ:
Count plots for categorical features:
plt.figure(figsize=(10, 6))
sns.countplot(x='categorical_column', data=df)
plt.show()
Python Interview Q&A: https://topmate.io/coding/898340
Like for more โค๏ธ
ENJOY LEARNING ๐๐
