What are the top resources for continuous learning in data science hackathons?

The best resource for continuous learning in data science hackathons is by competing. kaggle is the best platform where one can find lot of resources , new business problems, discussion forums and also a network of people with data science enthusiasts and experts. You can also find winners solutions for all the competitions where it is like golden resource for someone who wants to learn very qucikly without any real world exposure. Also please look at some other hackathon platforms like Zindi, AnalyticsVidhya, Machinehack, AICrowd etc....... Also we can do self learning by reading blogs, articles and research papers specific to business problem we are solving.

1 course that will make you an absolutely amazing Data Scientist is the Data Science Professional Certificate by IBM on the Coursera Platform. It has 10 courses within it. It will give you a very good idea of the field and help you move ahead. This is the first course you should start off with.

Top-class online courses help to master data science for hackathons. Modules on data analysis, visualization, ML algorithms (including deep learning) and NLP as well as computer vision are available from platforms such as Coursera, Udemy or edX. Do exercises, projects and get feedback from the experts to really learn something. These courses provide critical skills for winning hackathons.

Online courses are one of the best resources you can get to learn about data science from scratch. Instructor-led, real-time virtual classes give even better experience and value for the time and money invested. One of the best learning platforms for data science that I recommend is the Data Science Training led by Dr. Harry Patria. You can contact him directly through his profile if you are interested.

online courses have been invaluable for continuous learning in hackathons. Platforms like Coursera and Udemy provide structured content, interactive exercises, and expert guidance, enhancing my skills in data analysis, machine learning, and more. The flexibility of online courses accommodates the demands of hackathon participation, allowing me to acquire and apply knowledge effectively. The combination of theoretical understanding and hands-on projects from these courses significantly contributes to success in data science hackathons.

Blogs and posts are very helpful and always keeps you ahead on what is happening in the field. I read Towards Data Science, AI Tidbits newsletter, The Rundown Newsletter, Superintelligence Newsletter. For posts I usually follow people like Andrej Karpathy on Twitter, Sudalai (SRK) on LinkedIn, Sanyam Bhutani on LinkedIn. Because of them I am always updated on what's happening currently.

Keeping abreast of the latest in data science is efficiently done through blogs and podcasts by leading data scientists and industry practitioners. These platforms are a treasure trove of insights, practical tips, case studies, and interviews, offering a unique opportunity to learn from others' successes and pitfalls. They are excellent resources for discovering innovative ideas and methodologies for your projects. Renowned blogs and podcasts like Towards Data Science, Medium, KDnuggets, Data Science Central, Data Skeptic, DataFramed, and SuperDataScience are pivotal for anyone keen on delving deeper into the field, providing a blend of expert perspectives and cutting-edge trends

Build up your data science savvy by diving into influential blogs and podcasts. Experienced practitioners share their insights, case studies and interviews on sites like Towards Data Science, KDnuggets and DataFramed. Immerse yourself in their experiences, mistakes and successes to take away priceless lessons. Through these enlightening resources, get a handle on the latest trends and best practices in your projects.

For those who learn best through reading, books and papers are the best resource to learn. Personally, this is my go-to resource when I learn data science back in 2020. By reading books, I get to dive deeper into the concept behind the application and also better understand the syntax and logic presented as an example to accompany the concept.

I studied the following books that helped me for life and I am sure it will do the same for you : - The hundred Page Machine Learning by Andriy Burkov - Deeplearning by Ian Goodfellow For research papers I always head to arxiv and paperswithcode.com These will be very very helpful for whoever who wants to study through books and research papers.

Books and papers unpack the core of data science and machine learning. Fundamental concepts are illuminated in works such as 'Introduction to Statistical Learning' and 'The Elements of Statistical Learning'. Take a step further into application with 'Hands-on Machine Learning with Scikit-Learn and TensorFlow' or For more broad but also detailed reading, take in Deep Learning Or for an abridged read on the entirety of machine learning--The Hundred-Page Machine Learning Book. These resources help develop critical thinking skills, theoretical knowledge as well as problem-solving abilities.

Engaging with communities and forums is a dynamic way to advance in data science. These platforms enable you to mingle with fellow enthusiasts, exchange questions and ideas, showcase your projects, and gain valuable feedback. They're also a great resource for discovering opportunities such as hackathons, events, and connecting with mentors, collaborators, and potential employers. Active and supportive communities like Stack Overflow, Reddit, Quora, Data Science Stack Exchange, Kaggle, DataHack, and Analytics Vidhya offer a rich environment for learning, sharing, and growth, making them indispensable for anyone passionate about data science.

Communities are very very helpful. I am part of a AI community in India on whatsapp that has 300+ people and its very helpful. So be part of communities, the more local it is, the better. Apart from that there is the open forums like Kaggle, Twitter that helps me. If you are a student, be a part of the tech community in your college, I infact started one, when I was in college.

- Kaggle: The ultimate hub for data scientists, offering discussions, code sharing, and competitions, perfect for finding mentors and inspiration. - DataHack: Hosts frequent hackathons and maintains an active community forum for knowledge exchange and problem-solving. - Analytics Vidhya: Provides a platform for learning, collaboration, and participating in data science challenges and hackathons. - Reddit Data Science: A vibrant community for asking questions, sharing tips, and staying updated on the latest trends.

Within the thriving communities and forums, you can find your data science journey. Forums such as Stack Overflow, Reddit and Kaggle provide interactive spaces to exchange ideas, ask advice or showcase projects. Extend learning by participating in Data Science Stack Exchange and Analytics Vidhya communities of like-minded enthusiasts. Discover opportunities, collaborations and hackathons while linking up with mentors or prospective employers. Link up with these flourishing hubs to make your way and broaden the data spectrum.

Active participation in data science communities and forums has significantly enriched my learning experience in hackathons. Platforms like Stack Overflow, Reddit, and Kaggle provide a space to engage with fellow enthusiasts, seek advice, share projects, and receive valuable feedback. These communities foster a collaborative environment, offering insights into diverse approaches, tools, and solutions. Networking through these forums has opened doors to opportunities and connections, enhancing both my skills and overall experience in data science hackathons.

Engaging in data science projects and competitions has been instrumental in honing my practical skills. Platforms like Kaggle and DataHack provide real-world scenarios to apply theoretical knowledge, fostering hands-on experience in the end-to-end data science process. These experiences have not only improved my technical proficiency but also enhanced my problem-solving and collaboration skills. Participating in competitions offers valuable insights into diverse approaches and innovative solutions, contributing to continuous learning and growth in the field of data science.

Play with data, and enter the exciting world of competitively. You can try your hand at real-world problems on platforms such as Kaggle, DataHack and Zindi. Hone your skills from data preprocessing all the way up to model deployment. Participate in the full spectrum of data science work, and learn from various solutions and feedback. They not only test your strength, but give you a chance to demonstrate what you can do, and build up the portfolio that will tell anyone just how good they are. These arenas make the transition from theory to action, and get your data ride underway.

No doubt Kaggle is the best place for competition and doing projects. Github is also great where you get open source projects that you can go through, learn and try to replicate for practice. YouTube is super amazing, so many folks sharing their projects idea, some even building projects right from scratch. You can learn from there. Hackathon is best, there are always Hackathon happening in your city or college, participate in them.

For hackathon enthusiasts and data science buffs, Kaggle is a fantastic place to find data science projects. Hackathons are more than coding. It is teamwork, brainstorming, and rapidly bringing cool ideas to life and helps boost your confidence. But don't stop there! MLH, Devpost.com, and HackerEarth offer a variety of hackathons too. Also, keep tabs on big tech conferences. They often host hackathons. Such opportunities are invaluable for real-world experience.