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Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free For collaborations: @love_data
Show more📈 Analytical overview of Telegram channel Data Science & Machine Learning
Channel Data Science & Machine Learning (@datasciencefun) in the English language segment is an active participant. Currently, the community unites 75 822 subscribers, ranking 2 109 in the Education category and 4 254 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 75 822 subscribers.
According to the latest data from 20 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 833 over the last 30 days and by 1 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 3.15%. Within the first 24 hours after publication, content typically collects 1.15% reactions from the total number of subscribers.
- Post reach: On average, each post receives 2 391 views. Within the first day, a publication typically gains 875 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 3.
- Thematic interests: Content is focused on key topics such as learning, accuracy, distribution, panda, dataset.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free
For collaborations: @love_data”
Thanks to the high frequency of updates (latest data received on 21 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 Education category.
75 822
Subscribers
+124 hours
+1047 days
+83330 days
Posts Archive
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Handbook of Computer Programming with Python
Dimitrios Xanthidis, 2022
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Bayesian Statistical Modeling with Stan, R, and Python
Kentaro Matsuura, 2023
1. What is DBSCAN Clustering?
DBSCAN groups ‘densely grouped’ data points into a single cluster. It can identify clusters in large spatial datasets by looking at the local density of the data points. The most exciting feature of DBSCAN clustering is that it is robust to outliers. It also does not require the number of clusters to be told beforehand, unlike K-Means, where we have to specify the number of centroids.
2. What are the different forms of joins in a table?
SQL has many kinds of different joins including INNER JOIN, SELF JOIN, CROSS JOIN, and OUTER JOIN. In fact, each join type defines the way two tables are related in a query. OUTER JOINS can further be divided into LEFT OUTER JOINS, RIGHT OUTER JOINS, and FULL OUTER JOINS.
3.How is the grid search parameter different from the random search?
Model Hyperparameter tuning is very useful to enhance the performance of a machine learning model. The only difference between both the approaches is in grid search we define the combinations and do training of the model whereas in RandomizedSearchCV the model selects the combinations randomly. Both are very effective ways of tuning the parameters that increase the model generalizability.
Random search is a technique where random combinations of the hyperparameters are used to find the best solution for the built model. The drawback of random search is that it yields high variance during computing. Since the selection of parameters is completely random; and since no intelligence is used to sample these combinations, luck plays its role.
4.How should you maintain a deployed model?
A deployed model needs to be retrained after a while so as to improve the performance of the model. Since deployment, a track should be kept of the predictions made by the model and the truth values. Later this can be used to retrain the model with the new data. Also, root cause analysis for wrong predictions should be done.
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Repost from Coding Interview Resources
Data Science Interview
DS Interview Books, 2022
How to start learning Data Science?
There are many resources available to help you start learning data science, depending on your background and goals.
Here are a few steps you can take:
Develop a strong understanding of the basics of statistics and programming.
Learn Python or R programming languages, both are popular among data scientists.
Learn the basics of data manipulation and visualization with tools such as pandas and matplotlib.
Learn the basics of machine learning, such as linear regression and k-nearest neighbors, and practice applying them to real-world datasets.
Take online courses and tutorials, such as those offered by Coursera, edX, and DataCamp.
Practice by working on projects and participating in online data science competitions.
Get familiar with popular data science libraries such as numpy, scikit-learn, tensorflow, keras and pytorch.
It's a good idea to start with a solid foundation in statistics and programming, and then build on that foundation by learning the specific tools and techniques used in data science. As you gain experience, you can start working on more complex projects and exploring specialized areas of the field.
Hyperparameter Tuning for Machine and Deep Learning with R
Eva Bartz, 2023
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