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Data science is not just about algorithms and code. It's about rolling up your sleeves and diving into the nitty-gritty of real-world challenges, applying tools and methods to get insight out of data.
One stop solutions almost always don't work. Or as you probably heard, there is no free lunch.
That's why we share insight on latest trends, and discuss evergreen tools and solutions to various challenges that we normally face when applying data science in real world challenges.
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We cover everything from feature engineering and selection to dealing with imbalanced data, interpreting machine learning models, and mastering time series forecasting.
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