What do flight attendants, surgical teams, and successful data science project managers have in common? They all use a checklist. Why? Although every surgery, project, and flight is different, each has a repeatable pattern. And checklists can help remind us of each step and major consideration. Don’t reinvent the wheel. Start with a base data […]
So you just completed a machine learning model and got great results! Mission accomplished? Not quite. Rather, you’re probably still early in your journey because … “No machine learning model is valuable, unless it’s deployed to production.” – Luigi Patruno …And even after you deployed your model, you’re still not done. Rather, you probably want […]
As organizations continue to expand their data analysis competencies, the data science function is becoming more of a team sport with numerous team roles within the data science group. Of all these roles, the difference between the data analyst and data scientist role is perhaps the most confusing. Indeed, the roles have similar responsibilities. Data […]
A data science life cycle is an iterative set of data science steps you take to deliver a project or analysis. Because every data science project and team are different, every specific data science life cycle is different. However, most data science projects tend to flow through the same general life cycle of data science […]
The Boston Dynamics humanoid robot tripping over a curtain and tumbling off stage? A prediction model that tips a teen’s parents off to her pregnancy? A music recommendation engine that suggests Coldplay? Yup. There are just too many big data, data science, and data analytics failure examples to cover in just one post. Indeed, the […]
It’s an understatement that great leadership is challenging and rare. And leading data science teams has unique challenges: Stakeholders might get disillusioned by your team’s inability to deliver magic The battle to recruit and retain data science talent is fierce Data science’s ethical dilemmas are particularly perplexing There is not an agreed-upon process for managing […]
Data Science vs Software Engineering isn’t really a battle. Rather these are two somewhat overlapping and complementary fields that are similar in many ways. However, dig deeper in the discussion of data science vs software engineering, and you’ll find key differences in the two fields: Data science is more exploratory. Software engineers are more focused […]
So are there new emerging approaches that are data science native? Microsoft’s Team Data Science Process (TDSP), Domino Data Lab’s Data Science Life Cycle, and the Data Science Process Alliance’s Data Driven Scrum (DDS) are approaches that are both data science native and agile. There are pros and cons specific to each approach but they share some fundamental principles.
Can you mix and match elements of multiple project management approaches? Of course! This post explores two such general hybrid approaches for data science:agile-waterfall and research and development.
Agile has taken the software community by storm with Scrum and Kanban leading the way. What are these approaches and do the fit in the world of data science?