Method is all you need: 7 mistakes to avoid in Data Science

Once upon a time, data science was valuable only for a handful of Big Tech companies. Data science is now revolutionizing many "traditional" sectors: from automotive to finance, from real estate to energy. Research by PwC estimates that AI will contribute over 15.7 trillion US dollars to the global GDP by 2030 -- for reference, the GDP of the Eurozone in 2018 was worth 16 trillion dollars [1]. All businesses now perceive their data as assets and the insights they can gain as a competitive advantage. Yet, more than 80% of all data science project fails [2]. Each failed project fails for its own peculiar reasons, but, in three years of experience, we noticed some patterns.