Data Science hackathons come with the ‘problem’. While finding a solution to your problem is one thing, solving another’s problem, is quite another.
Building a hypothesis set is crucial for building a data science project, for it is important to see what you need to see.
Team building and collaboration lie at the core of the hackathon. Identifying people with unique skills, who can perform under pressure is crucial for getting the wheels rolling.
You may be a good programmer but that doesn’t mean you need to build everything from scratch.
Data science is about building predictive models and they require insights extracted from data. Feature engineering is exactly about this aspect.
Ensemble modelling is nothing but mixing data from different models to improve the stability and predictive capacity of the existing machine learning model.
Not having a validation framework is like punching in the dark. To ensure your model is robust and reliable, it should be tested against various subsets.
Building a data science project involves coding from the scratch or using codes from different sources.
Data model building is a linear process that involves data cleaning, EDA, feature engineering, model building & evaluation, a framework that most programmers.
Communication, both internal and external, is very critical, especially in a fast-paced and process-driven environment.
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