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Data science/ML/AI

Data science/ML/AI

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Data science and machine learning hub Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources. For beginners, data scientists and ML engineers πŸ‘‰ https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatascientist

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Programming, data science, ML - free courses by Big Data SpecialistNetwork:Programming, data science, ML - free courses by Big Data SpecialistIndia31 607Technologies & Applications9 380

πŸ“ˆ Analytical overview of Telegram channel Data science/ML/AI

Channel Data science/ML/AI (@datascience_bds) in the English language segment is an active participant. Currently, the community unites 13 674 subscribers, ranking 9 380 in the Technologies & Applications category and 31 607 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 13 674 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 143 over the last 30 days and by 2 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 8.09%. Within the first 24 hours after publication, content typically collects 2.22% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 1 106 views. Within the first day, a publication typically gains 304 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 5.
  • Thematic interests: Content is focused on key topics such as panda, learning, row, api, ethic.

πŸ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
β€œData science and machine learning hub Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources. For beginners, data scientists and ML engineers πŸ‘‰ https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatasci...”

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.

13 674
Subscribers
+224 hours
+217 days
+14330 days
Posts Archive
Data science/ML/AI
Data science/ML/AI
13 677
photo content

Data science/ML/AI
Data science/ML/AI
13 677
photo content

Data science/ML/AI
Data science/ML/AI
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photo content

Data science/ML/AI
Data science/ML/AI
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Detailed roadmap for Data Science
Detailed roadmap for Data Science

Data science/ML/AI
Data science/ML/AI
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Learn ETL using SSIS Microsoft SQL Server Integration Services (SSIS) Training Rating ⭐️: 4.6 out 5 Students πŸ‘¨β€πŸŽ“ : 62,785 Duration ⏰ : 1hr 37min on-demand video Created by πŸ‘¨β€πŸ«: Rakesh Gopalakrishnan πŸ”— Course Link #ETL #SSIS βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– πŸ‘‰Join @bigdataspecialist for moreπŸ‘ˆ

Data science/ML/AI
Data science/ML/AI
13 677
πŸ”₯FREE COURSE ON GENERATIVE AIπŸ”₯ Interested in learning about GENERATIVE AI?πŸ”₯ Here's a free course from Google. Link #genera
πŸ”₯FREE COURSE ON GENERATIVE AIπŸ”₯ Interested in learning about GENERATIVE AI?πŸ”₯ Here's a free course from Google. Link #generative ai #ml #ai βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

Data science/ML/AI
Data science/ML/AI
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Repost from Programming, data science, ML - free courses by Big Data Specialist
πŸ“Š Data Scientists vs Software Engineers πŸ–₯ πŸ” Ever wondered what sets apart Data Scientists from Software Engineers? Let's dive into the key differences! πŸ“ˆ Data Scientists: πŸ’‘ Their role revolves around analyzing complex data to extract valuable insights. πŸ” They focus on data analysis, modeling, and visualization to uncover patterns and trends. 🧠 Skills include statistics, machine learning, and data mining. πŸ”§ Tools they commonly use are Python, R, SQL, and Jupyter Notebooks. πŸ“‹ Responsibilities include data cleaning, preprocessing, and transformation. 🌐 They often possess a strong domain knowledge in a specific industry or business area. 🎯 Their goal is to extract actionable insights from data to drive decision-making. πŸ”„ Workflow follows CRISP-DM, a standard process for data mining. πŸ’Ό Project examples include predictive modeling and recommendation systems. πŸš€ Deployment involves integrating models and insights into existing systems or presenting them in reports. 🎯 Performance evaluation focuses on metrics like accuracy, precision, recall, and F1 score. 🀝 Collaboration involves working with cross-functional teams including domain experts and stakeholders. πŸ’» Software Engineers: πŸ’‘ Their role centers around designing, developing, and maintaining software systems. πŸ” They focus on software design, coding, and testing to create functional and reliable solutions. 🧠 Skills include programming languages, algorithms, and databases. πŸ”§ Tools they commonly use are Java, C++, JavaScript, IDEs, and version control systems. πŸ“‹ Responsibilities include developing scalable software applications. 🌐 They possess general knowledge of software engineering principles. 🎯 Their goal is to develop software that meets user needs and operates flawlessly. πŸ”„ Workflow follows agile or waterfall software development methodologies. πŸ’Ό Project examples include web or mobile app development and system integration. πŸš€ Deployment involves delivering software for end-users to interact with directly. 🎯 Performance evaluation focuses on code efficiency, reliability, and scalability. 🀝 Collaboration involves working with other software engineers and project managers. πŸš€ Whether extracting insights from data or building robust software systems, both Data Scientists and Software Engineers play essential roles in the digital landscape! πŸ”₯ Let's celebrate their unique skills and contributions to the world of technology! πŸ’ͺπŸ’» #DataScience #SoftwareEngineering #TechComparison #DigitalWorld #DataAnalysis #SoftwareDevelopment βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– πŸ‘‰Join @bigdataspecialist for moreπŸ‘ˆ

Data science/ML/AI
Data science/ML/AI
13 677
Data science cheatsheet
Data science cheatsheet

Data science/ML/AI
Data science/ML/AI
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Basic terms for beginners
Basic terms for beginners

Data science/ML/AI
Data science/ML/AI
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Data Science Pipeline βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @datascience_bds for more cool data science materials. *This channel belongs to @bi
Data Science Pipeline βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

Data science/ML/AI
Data science/ML/AI
13 677
Artificial Neural Network for Regression Rating ⭐️: 4.6 out of 5 Duration ⏰: 1hr 11min on-demand video Students πŸ‘¨β€πŸ«: 49,827 Created by: Hadelin de Ponteves, SuperDataScience Team, Ligency Team πŸ”— Course link #ai #ml #neural_networks #machine_learning #data_science #regression βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

Data science/ML/AI
Data science/ML/AI
13 677
Data Science vs ML vs Data Analytics vs Math Visualization created by our team. #datascience βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– πŸ‘‰Join @datascien
Data Science vs ML vs Data Analytics vs Math Visualization created by our team. #datascience βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– πŸ‘‰Join @datascience_bds for moreπŸ‘ˆ

Data science/ML/AI
Data science/ML/AI
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Business_Science_Problem_Framework.pdf2.63 KB

Data science/ML/AI
Data science/ML/AI
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data-science-ipython-notebooks Creator: Donne Martin Stars ⭐️: 22.6k Forked By: 7k GithubRepo: https://github.com/donnemartin/data-science-ipython-notebooks βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @datascience_bds for more cool repositories. *This channel belongs to @bigdataspecialist group

Data science/ML/AI
Data science/ML/AI
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Visualisation: visual representations of data and information Modern society is often referred to as 'the information society
Visualisation: visual representations of data and information Modern society is often referred to as 'the information society' - but how can we make sense of all the information we are bombarded with? In this free course, Visualisation: visual representations of data and information, you will learn how to interpret, and in some cases create, visual representations of data and information that help us to see things in a different way. ⏰ Free Online Course ⏰ 9 Module ⏰ Duration : 8 hours πŸƒβ€β™‚οΈ Self paced Offered by: openlearn πŸ”— Course link #Data #Visualization #data_science βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– πŸ‘‰Join @datascience_bds for moreπŸ‘ˆ

Data science/ML/AI
Data science/ML/AI
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Applied Data Science by Daniel Krasner πŸ“„ 141 pages πŸ”— Book link #BigData #DataScience #MachineLearning #Statistics βž–βž–βž–βž–βž–βž–βž–βž–βž–
Applied Data Science by Daniel Krasner πŸ“„ 141 pages πŸ”— Book link #BigData  #DataScience  #MachineLearning  #Statistics βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @datascience_bds for more

Data science/ML/AI
Data science/ML/AI
13 677
NOC:Python for Data Science, IIT Madras πŸ†“ Free Online Course πŸ’» 40 Lecture Videos ⏰ 5 Module πŸƒβ€β™‚οΈ Self paced Teacher πŸ‘¨β€πŸ« : Prof. Ragunathan Rengasamy πŸ”— https://nptel.ac.in/courses/106106212 #Data_Science #IIT βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– πŸ‘‰Join @bigdataspecialist for moreπŸ‘ˆ

Data science/ML/AI
Data science/ML/AI
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6 Deep Learning Books
6 Deep Learning Books

Data science/ML/AI
Data science/ML/AI
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Repost from AI Revolution
Evolution of AI
Evolution of AI

Data science/ML/AI
Data science/ML/AI
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Different Probability Distributions used in Data Science
Different Probability Distributions used in Data Science