Webinar: How To Crack Data Science Hackathons

Hackathons have evolved to be one of the key ways for data scientists to learn new skills, practically apply their skills, get a hands-on solving business-centric problem, build a community, and more. Not just a way for boosting the skills, hackathons have also become one of the crucial tools for companies to hire candidates for internships and even full-time employment. 

Addressing the growing popularity of hackathons, there are several hackathon platforms available today for data science enthusiasts to compete on. While there are many resources available, it can be overwhelming for candidates, especially newcomers to fit right into hackathons. There can be challenges such as finding the right project to work according to the skills to even becoming a part of the team. 

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To address some of the commonly faced challenges that participants face such as the kind of coding skills required, picking the right platform, approaching the problem-statement, and more, Analytics India Magazine in association with MachineHack will be conducting a webinar on ‘How to crack data science hackathons’. 

It will be conducted by Devrup Banerjee, PGDM, Great Lakes. He is also a GrandMaster at the Machinehack platform. Banerjee will take the participants through the nuances of cracking data science hackathons. He will take through pointers such as: 

  • Best practises to follow while participating in a data science hackathon
  • How to approach a problem-statement
  • How to best utilise the resources and knowledge you have to crack the hackathon
  • How to pick the right hackathon platform to compete
  • Q&A with the participants

Details of the webinar

Speaker: Devrup Banerjee, PGDM, Great Lakes, Machinehack GM

Timing: 3rd October, 11:00 AM

Duration: 1.5 hours including Q&A

Anurag Upadhyaya
Experienced Data Scientist with a demonstrated history of working in Industrial IOT (IIOT), Industry 4.0, Power Systems and Manufacturing domain. I have experience in designing robust solutions for various clients using Machine Learning, Artificial Intelligence, and Deep Learning. I have been instrumental in developing end to end solutions from scratch and deploying them independently at scale.

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