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Python for Data Analysts

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

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πŸ“ˆ 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 51 492 subscribers, ranking 2 607 in the Technologies & Applications category and 7 356 in the India region.

πŸ“Š Audience metrics and dynamics

Since its creation on Π½Π΅Π²Ρ–Π΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 51 492 subscribers.

According to the latest data from 08 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 204 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 5.19%. 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 2 670 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 9.
  • Thematic interests: Content is focused on key topics such as visualization, panda, analyst, sql, analytic.

πŸ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
β€œFind top Python resources from global universities, cool projects, and learning materials for data analytics. For promotions: @coderfun Useful links: heylink.me/DataAnalytics”

Thanks to the high frequency of updates (latest data received on 09 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.

51 492
Subscribers
-1624 hours
+447 days
+20430 days
Posts Archive
18 - Working with Text Files in Python

17 - Hands-On Coding Challenges - Functions

16 - Functions in Python

15 - Hands-On Challenges Sets and Dictionaries

14 - Dictionaries in Python

13 - Sets and Frozensets in Python

12 - Tuples in Python

11 - Hands-On Challenges Lists

10 - Lists in Python

09 - Hands-On Challenges Flow Control and Loops

08 - Python Loops

07 - Program Flow Control in Python

06 - Hands-On Challenges Python Strings

05 - Strings in Python

04 - Hands-On Challenges Python Basics

03 - Python Basics

02 - Setup the Programming Environment

01 - Course Introduction

πŸ”° Python Programming: The Complete Python Bootcamp 2023 https://t.me/pythonanalyst/59 🌟 4.4 - 1838 votes πŸ’° Original Price:
πŸ”° Python Programming: The Complete Python Bootcamp 2023 https://t.me/pythonanalyst/59 🌟 4.4 - 1838 votes πŸ’° Original Price: $74.99 Python from Scratch. Learn Data Science and Visualization, Automation, Excel, SQL and Scraping with Python.100% Hands-On Taught By: Andrei Dumitrescu, Crystal Mind Academy Download Full Course: https://t.me/pythonanalyst/59 Download All Courses: https://t.me/pythonfreebootcamp

πŸ“ˆ Predictive Modeling for Future Stock Prices in Python: A Step-by-Step Guide The process of building a stock price predicti
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πŸ“ˆ Predictive Modeling for Future Stock Prices in Python: A Step-by-Step Guide The process of building a stock price prediction model using Python. 1. Import required modules 2. Obtaining historical data on stock prices 3. Selection of features. 4. Definition of features and target variable 5. Preparing data for training 6. Separation of data into training and test sets 7. Building and training the model 8. Making forecasts 9. Trading Strategy Testing