Data Science Shop

Data Science has a roadmap, but no one has spent the time to lay it out and make it accessible to the people that would benefit most from it. The Data Science Shop fills that gap. #datasciecneshop #datascience #dataproducts #dataengineering #mlengineering #dataproductcycle

Innovation often comes from systematizing existing knowledge. Two historical examples show the path ahead for #DataScience: take a discipline in the craft stage, create comprehensive guides with clear methodologies, and establish a common language for the profession. Read in our #Substack how the #datascienceshop project is pushing the envelope: https://lnkd.in/eAjYzGcW Marco A Morales, PhD #dataproducts #dataengineering #mlengineering

The evolution stage of the #DataProductCycle is all about ensuring the #DataProduct remains effective over time by identifying unattended business needs and incorporating new features. Read more in our #Substack: https://lnkd.in/ema7kBCu Marco A Morales, PhD #DataScienceShop #dataproducts #dataengineering #mlengineering

The adoption stage of the #DataProductCycle focuses on identifying potential obstacles to adoption and creating strategies to overcome them. Read more in our #Substack: https://lnkd.in/ema7kBCu Marco A Morales, PhD #DataScienceShop #dataproducts #dataengineering #mlengineering

The development and implementation stage is the most technically creative part of the #DataProductCycle, often associated with algorithms, #machinelearning, and #AI. Read more in our #Substack: https://lnkd.in/ema7kBCu Marco A Morales, PhD #DataScienceShop #datascience #dataengineering #mlengineering

The Diagnosis Stage of the #DataProductCycle stage is critical because it determines whether a #DataProduct is even feasible. If the problem isn't clearly defined or the data isn't appropriate, the entire project could be derailed before it begins. Read more in our #Substack: https://lnkd.in/ema7kBCu Marco A Morales, PhD #DataScienceShop #datascience #dataengineering #mlengineering

Each of the 4 stages of the #DataProductCycle involves different members of the #DataScienceShop crew, working together to bring a #DataProduct from concept to reality. Read more in our #Substack: https://lnkd.in/ema7kBCu Marco A Morales, PhD #dataproducts #dataengineering #mlengineering #datascience

The #DataProductCycle consists of four stages: Diagnosis (defining the problem and assessing data), Development/Implementation (designing and building the solution), Adoption (overcoming obstacles to use), and Evolution (keeping the product relevant) Read more in our #Substack: https://lnkd.in/ema7kBCu Marco A Morales, PhD #DataScienceShop #datascience #dataengineering #mlengineering

The final stage in the #DataProductCycle is not really an endpoint, but rather the beginning of a new cycle. Read more in our #Substack: https://lnkd.in/ema7kBCu Marco A Morales, PhD #Datascience #DataScienceShop #dataproducts #dataengineering #mlengineering #dataproducts

A #DataProduct is only valuable if it's used. The adoption stage of the #DataProductCycle is crucial in ensuring that the solution doesn't just gather dust but becomes an integral part of business operations. Read more in our #Substack: https://lnkd.in/ema7kBCu Marco A Morales, PhD #DataScienceShop #dataproducts #dataengineering #mlengineering