Summary: To stay on top of your personal development, try learning new things like a programming language, an instrument, or exposure to a new field (e.g. biology or accounting). Exposure to new ideas helps you avoid confirmation bias and increase you willingness to explore your analysis further.
Summary: Keep analyses organized with a directory that supports exploratory and production results (scripts, visualizations, and models) and keep data immutable. Use a tool like CookieCutter Data Science to automatically build a directory structure.
Summary: Every organization should provide their analysts and data scientists with a few key tools: A Data Dictionary, a Metric Dictionary, a Research Repository, and a Code Repository. All of these tools need to be searchable to make it easy for analysts to find and use previous work.
Summary: Advanced analyses can be simplified by calling out which variables are most important. Decision Trees, Random Forests, Regression, and Chi-Square tests can quickly reveal what variables carry a lot of weight.
Summary: Always check your numbers with smaller, simpler queries and figures. Use total sales as a reality check for comparison to sales queries. When creating models, compare performance to a simpler model. Don’t assume complexity equals accuracy. Be prepared to compare against existing “gold standard” models.