Why Scrum is awful for Data Science. More and more data science teams seem to be hopping on the scrum bandwagon, but is it a good idea?
Scrum is a popular methodology for PM in software engineering and recently the trend has carried over to data science. While the utility of Scrum in standard software engineering may remain up for debate, here I will detail why it has unquestionably no place in data science (and data engineering as well). This is not to say that “Agile” as a whole is bad for data science, but rather that the specific principles of Scrum: sprints, single product owner, scrum master, daily stand-ups (and the litany of other meetings) fit poorly for data science teams and ultimately result in poorer products.
Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
In this post, we’ll walk through several types of data science projects, including data visualization projects, data cleaning projects, and machine learning projects, and identify good places to find datasets for each.
Need a data set to practice with? Data Science Dojo has created an archive of 32 data sets for you to use to practice and improve your skills as a data scientist.
How to Plan and Organize a Data Science/Analytics Project? Conducting a data science/analytics project always takes time and has never been easy.
Simple steps to improve your data science projects and get noticed