Data Science Blogathon - 12
About the Event
The extremely popular and much anticipated Data Science Blogathon returns!
What if you could earn money for every article you publish in the blogathon? That’s the incredible opportunity in front of you!
With over 1300+ articles so far getting thousands of views, we continue to receive an overwhelming response from the community!
What are the Prizes on offer?
Here’s what you stand to win as the base reward:
Submission Type |
Minimum Length of Text(Code not Included) |
Views |
Topic |
Base Reward for Creators Club Members |
Base Reward for non members |
Article |
1000 |
More than 500 |
Deep Learning, CV, NLP, Data Engineering, MLOps |
1500 |
1500 |
Any Other Topic |
1000 |
1000 |
|||
Guide |
2500 |
No constraint |
No Constraint(please refer to suggestions below) |
7500 |
7500 |
* Only creators Club members are eligible to receive the base reward if their articles hve not crossed 500 views
* Note that the article will have to be technical and code-based in nature to be eligible for the above special categories. Listicles or career articles will not count.
The top 3 winners will be judged based on the number of unique pageviews on their articles.
1st Prize: INR 10,000
2nd Prize: INR 5,000
3rd Prize: INR 3,000
And that’s not all! More Prizes!
We always appreciate the extra effort our community members put in, so if your article generates:
- More than 15000 views, you get INR 2,000 as a bonus,
- More than 5000 views, you get INR 1,000 as a bonus
regardless of where you finish in the above category!
A New Submission Category - Guides!
As mentioned above, we are introducing a new type of submission this time, called “Guides”. Since we receive multiple entries for the ‘Guides’ category, only one Guide per topic will be published.
Here is an example of a Guide: K Means Clustering | K Means Clustering Algorithm in Python
We know writing one can be a daunting task, so we have provided a list of suggested topics to get you started:
Guide Topics |
Guide Topics |
Guide Topics |
Guide Topics |
End-to-End Machine Learning Model using Julia |
A Comprehensive Guide on Building an ETL Pipeline for Beginners |
A Detailed Case Study using Geospatial Analysis |
A Complete Guide on Sampling Techniques |
How to deal with Missing Values |
A Comprehensive Guide on Docker |
A Complete Guide on ggplot |
A Complete Guide on Hyperparameter Tuning in Deep Learning |
A Comprehensive Guide on Building Bots using Python |
A Complete Guide on Kubernetes |
A Detailed Study on Covid-19 Vaccinations data |
How to deal with Sparse Datasets |
Building an End-to-End Multiclass Text Classification Model |
A Comprehensive Guide on using Django for Data Science |
End-to-End Predictive Analysis on AirBnB |
A Comprehensive Guide on Causal Inference |
A Comprehensive Guide on Building Chatbots |
A Comprehensive Guide on using AWS for Data Science |
End-to-End Predictive Analysis on Zomato |
A Comprehensive Guide on Graph Neural Networks |
A Comprehensive Guide on Recommendation Engines |
A Comprehensive Guide on Databricks |
A Comprehensive Guide on ML Interpretability Techniques and Tools |
A Comprehensive Guide on Optimizers in Deep Learning |
A Comprehensive Guide on Reinforcement Learning |
A Comprehensive Guide on using RedShift |
A Comprehensive Guide on Replication in Data Engineering |
A Comprehensive Guide on Federated Learning |
Building an End-to-End Polynomial Regression Model |
A Detailed Guide on SQL Query Optimisation |
A Comprehensive Guide on Sharding in Data Engg. |
A Comprehensive Guide on Neo4j |
A Comprehensive Guide on Markov Chain |
A Complete Guide on using MongoDB for Data Science |
A Comprehensive Guide on Partitioning in Data Engg. |
A Complete Guide on TextBlob |
An End-to-end Guide on Anomaly Detection |
A Complete Guide on Heroku for Deploying ML Models |
A Comprehensive Guide on Granger Causality Test |
A Comprehensive Guide on using AzureML |
Building an End-to-End Logistic Regression Model |
A Comprehensive Guide on using KNIME |
A Complete Guide on Tableau |
A Comprehensive Guide on Market Basket Analysis |
A Comprehensive Guide on Feature Engineering |
A Comprehensive Guide on Optuna |
A Complete Guide on PowerBI |
|
A Comprehensive Guide on Machine Learning for Mobile Devices |
A Comprehensive Guide on using Flask for Data Science |
A Comprehensive Guide on Microsoft Excel for Data Analysis |
Feel free to explore any topic of your choice though - the only restriction is that it should be as comprehensive as possible and should be of minimum 2500 words in length.
A Special Award Category - Win the new iPad!
You read that right - you even stand an opportunity to win the new iPad! Every author in the blogathon will be eligible to win the new iPad through a lucky draw where a single winner will be chosen!
This is an unmissable opportunity to get your hands on the latest Apple iPad that has the world swooning. Imagine the writing and reading you’ll be able to accomplish on this blazing fast tablet.
How do I Participate?
To enter the competition, just press the register button above. Once the competition starts on September 16th, head over tohttps://editor.analyticsvidhya.com/ and start writing. It’s that simple!
Note that it takes us upto 36 hours to review and provide feedback for each article. And once you see ‘Published’ on the Editor, give it up to 6 hours for the article to reflect on the Analytics Vidhya blog.
Please make sure to add a couple of lines about yourself and a link to your social media such as LinkedIn, etc. at the end of your article for better visibility.
The leaderboard will reflect one article per author. If you want to submit 2 or more articles, those will count as separate entries.
The submissions will close on 7th October 2021, 23:59 IST (GMT +5:30 hrs). We will provide an extra week to work on any feedback we give, provided the article has been submitted before the deadline.
Even though you need to submit your article by 11.59 pm on October 7th the views for your article will count until October 14th, 11.59 pm. So keep sharing your article after it's been published to increase your chances of winning!
Note: We update the leaderboard twice a day. There is no set time for the update per se. But check back in the afternoon and late evening to see the latest views.
When will the winners be declared?
The winners will be announced on October 15th, 2021. You can view the leaderboard tab to see the ranking for the number of views.
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Frequently Asked Questions
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Participants benefit from one-on-one feedback, publication on a respected platform, recognition from a global audience, and monetary rewards for each published article. Additionally, the top articles receive special rewards.
Each article must be original, and pass plagiarism and not AI generated content checks. You can submit multiple articles as long as each is distinct. Proper citation of all references and image sources is mandatory.
There are no specific requirements to register for the hackathon, although it is recommended to have some basic knowledge of the relevant topics, such as Data Science, Machine Learning, or Deep Learning, along with proficiency in a coding language, preferably Python.
In the Blogathon, an article typically explores a specific topic or idea within Data Science or Generative AI and is required to be at least 1000 words long. A guide, on the other hand, is a more comprehensive resource, covering all aspects of a particular subject in data science, and must be at least 2500 words long. Guides aim to serve as a one-stop resource, providing detailed insights and practical applications, whereas articles might focus on narrower or more specific topics.
Depending on the type of competition, you can participate individually or in a team.
Multiple submissions of the same article are prohibited and could lead to disqualification. Articles failing to meet the required length, originality, or citation standards will be rejected.
AVCC is a community for authors who have had three or more articles published in the Blogathons. Members benefit from monetary rewards for each published article and get the opportunity to showcase their work to a larger audience.
You can access the problem statement under the "Problem Statement" tab once the Hackathon is live.
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