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 51 508 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 51 508 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.
51 508
Subscribers
+2224 hours
+627 days
+25530 days
Posts Archive
51 508
๐๐ฅ๐๐ ๐ช๐ฒ๐ฏ๐๐ถ๐๐ฒ๐ ๐ง๐ผ ๐ ๐ฎ๐๐๐ฒ๐ฟ ๐๐ผ๐ฑ๐ถ๐ป๐ด ๐๐ผ๐ฟ ๐๐ฅ๐๐ ๐
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51 508
For data analysts working with Python, mastering these top 10 concepts is essential:
1. Data Structures: Understand fundamental data structures like lists, dictionaries, tuples, and sets, as well as libraries like NumPy and Pandas for more advanced data manipulation.
2. Data Cleaning and Preprocessing: Learn techniques for cleaning and preprocessing data, including handling missing values, removing duplicates, and standardizing data formats.
3. Exploratory Data Analysis (EDA): Use libraries like Pandas, Matplotlib, and Seaborn to perform EDA, visualize data distributions, identify patterns, and explore relationships between variables.
4. Data Visualization: Master visualization libraries such as Matplotlib, Seaborn, and Plotly to create various plots and charts for effective data communication and storytelling.
5. Statistical Analysis: Gain proficiency in statistical concepts and methods for analyzing data distributions, conducting hypothesis tests, and deriving insights from data.
6. Machine Learning Basics: Familiarize yourself with machine learning algorithms and techniques for regression, classification, clustering, and dimensionality reduction using libraries like Scikit-learn.
7. Data Manipulation with Pandas: Learn advanced data manipulation techniques using Pandas, including merging, grouping, pivoting, and reshaping datasets.
8. Data Wrangling with Regular Expressions: Understand how to use regular expressions (regex) in Python to extract, clean, and manipulate text data efficiently.
9. SQL and Database Integration: Acquire basic SQL skills for querying databases directly from Python using libraries like SQLAlchemy or integrating with databases such as SQLite or MySQL.
10. Web Scraping and API Integration: Explore methods for retrieving data from websites using web scraping libraries like BeautifulSoup or interacting with APIs to access and analyze data from various sources.
Give credits while sharing: https://t.me/pythonanalyst
ENJOY LEARNING ๐๐
51 508
๐ฃ๐ผ๐๐ฒ๐ฟ ๐๐ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐ ๐ค๐๐ฒ๐๐๐ถ๐ผ๐ป๐ ๐ง๐ผ ๐๐ฟ๐ฎ๐ฐ๐ธ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐ ๐
๐ก Preparing for a Power BI interview can feel overwhelming, but the right questions can make all the difference!
Here are 15 must-know Power BI interview questions that will boost your confidence and help you shine in front of hiring managers.
๐๐ข๐ง๐ค ๐:-
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All The Best๐
51 508
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
51 508
Exploratory Data Analysis (EDA) in Python involves a variety of techniques and tools to summarize, visualize, and understand the structure of a dataset. Here are some common EDA techniques using Python, along with relevant libraries:
51 508
๐๐ฃ ๐ ๐ผ๐ฟ๐ด๐ฎ๐ป ๐๐ฅ๐๐ ๐ฉ๐ถ๐ฟ๐๐๐ฎ๐น ๐๐ป๐๐ฒ๐ฟ๐ป๐๐ต๐ถ๐ฝ ๐ฃ๐ฟ๐ผ๐ด๐ฟ๐ฎ๐บ๐๐
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51 508
๐๐จ๐ฉ ๐๐๐๐ฌ & ๐๐ญ๐๐ซ๐ญ๐ฎ๐ฉ ๐๐จ๐ฆ๐ฉ๐๐ง๐ข๐๐ฌ ๐๐ข๐ซ๐ข๐ง๐ ๐ฅ
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51 508
๐ด Data Types in NumPy
๐ Arithmetic operations in Numpy
โก๏ธ+ ->np.add ->Addition(1+1=2)
โก๏ธ- ->np.substract ->Subtract(2-2=0)
โก๏ธ- ->np.negative - >Unary negative(-2)
โก๏ธ*->np.multiply->Multiplication(2*3=6)
โก๏ธ/->np.divide->Division(3/2=1.5)
โก๏ธ//->np.floor-divide - Floor divisor(3//2=1)
โก๏ธ->np.power->exponention(23)
โก๏ธ%->np.mod->modulus/remainder(9%4=1)
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Data Analyst vs. Data Scientist - What's the Difference?
1. Data Analyst:
- Role: Focuses on interpreting and analyzing data to help businesses make informed decisions.
- Skills: Proficiency in SQL, Excel, data visualization tools (Tableau, Power BI), and basic statistical analysis.
- Responsibilities: Data cleaning, performing EDA, creating reports and dashboards, and communicating insights to stakeholders.
2. Data Scientist:
- Role: Involves building predictive models, applying machine learning algorithms, and deriving deeper insights from data.
- Skills: Strong programming skills (Python, R), machine learning, advanced statistics, and knowledge of big data technologies (Hadoop, Spark).
- Responsibilities: Data modeling, developing machine learning models, performing advanced analytics, and deploying models into production.
3. Key Differences:
- Focus: Data Analysts are more focused on interpreting existing data, while Data Scientists are involved in creating new data-driven solutions.
- Tools: Analysts typically use SQL, Excel, and BI tools, while Data Scientists work with programming languages, machine learning frameworks, and big data tools.
- Outcomes: Analysts provide insights and recommendations, whereas Scientists build models that predict future trends and automate decisions.
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Like this post if you need more ๐โค๏ธ
Hope it helps ๐
51 508
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51 508
For data analysts working with Python, mastering these top 10 concepts is essential:
1. Data Structures: Understand fundamental data structures like lists, dictionaries, tuples, and sets, as well as libraries like NumPy and Pandas for more advanced data manipulation.
2. Data Cleaning and Preprocessing: Learn techniques for cleaning and preprocessing data, including handling missing values, removing duplicates, and standardizing data formats.
3. Exploratory Data Analysis (EDA): Use libraries like Pandas, Matplotlib, and Seaborn to perform EDA, visualize data distributions, identify patterns, and explore relationships between variables.
4. Data Visualization: Master visualization libraries such as Matplotlib, Seaborn, and Plotly to create various plots and charts for effective data communication and storytelling.
5. Statistical Analysis: Gain proficiency in statistical concepts and methods for analyzing data distributions, conducting hypothesis tests, and deriving insights from data.
6. Machine Learning Basics: Familiarize yourself with machine learning algorithms and techniques for regression, classification, clustering, and dimensionality reduction using libraries like Scikit-learn.
7. Data Manipulation with Pandas: Learn advanced data manipulation techniques using Pandas, including merging, grouping, pivoting, and reshaping datasets.
8. Data Wrangling with Regular Expressions: Understand how to use regular expressions (regex) in Python to extract, clean, and manipulate text data efficiently.
9. SQL and Database Integration: Acquire basic SQL skills for querying databases directly from Python using libraries like SQLAlchemy or integrating with databases such as SQLite or MySQL.
10. Web Scraping and API Integration: Explore methods for retrieving data from websites using web scraping libraries like BeautifulSoup or interacting with APIs to access and analyze data from various sources.
Give credits while sharing: https://t.me/pythonanalyst
ENJOY LEARNING ๐๐
51 508
๐ฏ ๐๐ฟ๐ฒ๐ฒ ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐๐ผ ๐๐ฒ๐๐ฒ๐น ๐จ๐ฝ ๐ฌ๐ผ๐๐ฟ ๐ง๐ฒ๐ฐ๐ต ๐ฆ๐ธ๐ถ๐น๐น๐ ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฑ๐
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51 508
Learning Python in 2024 is like discovering a treasure chest ๐ full of magical powers! Here's why it's valuable:
1. Versatility ๐: Python is used in web development, data analysis, artificial intelligence, machine learning, automation, and more. Whatever your interest, Python has an option for it.
2. Ease of Learning ๐: Python's syntax is as clear as a sunny day!โ๏ธ Its simple and readable syntax makes it beginner-friendly, perfect for aspiring programmers of all levels.
3. Community Support ๐ค: Python has a vast community of programmers ready to help! Whether you're stuck on a problem or looking for guidance, there are countless forums, tutorials, and resources to tap into.
4. Job Opportunities ๐ผ: Companies are constantly seeking Python wizards to join their ranks! From tech giants to startups, the demand for Python skills is abundant.๐ฅ
5. Future-proofing ๐ฎ: With its widespread adoption and continuous growth, learning Python now sets you up for success in the ever-evolving world of tech.
6. Fun Projects ๐: Python makes coding feel like brewing potions! From creating games ๐ฎ to building robots ๐ค, the possibilities are endless.
So grab your keyboard and embark on a Python adventure! It's not just learning a language, it's unlocking a world of endless possibilities.
#techinfo
51 508
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