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Data Science Workflow

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Data science workflow is a non-linear, iterative process, and that there are many skills and tools required to cover the full data science process

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Data Science Workflow

  1. 1. Hypothesis Objective Data Acquisition Exploration Cleaning Analysis Modeling Visualization Results Deployment Strategy Who is your client? What is the problem you are trying to solve? Collect the raw data needed to solve the problem i.e. - databases - crawling - streams - binaries - APIs - and so on Clean the data to convert it to a form that you can further analyze i.e. - parsing - cleaning - converting - transforming - and so on Perform in- depth analysis i.e. - descriptive - explorative - machine learning - statistical models - algorithms - and so on Communicate results of the analysis i.e. - visualizations - storytelling - interactive plots - dashboards - data apps - usability - and so on Transform your results into a data product i.e. - data engineering - architecture - usage patterns - security - and so on Domain-Know Data Science

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