Different data scientists have different processes for conducting their projects. And different types of projects require different steps. However, most data science projects flow through a similar workflow. One popular representation of this workflow is called OSEMN (pronounced “awesome”). Whether you use this or another life cycle, understanding the basic […]
Read more
A data science team’s process is a key driver to their projects’ success. However, as will be discussed below, there is not an existing AI / data science maturity model that is focused on how to evaluate (and improve) a team’s process. Hence, after reviewing several Analytics / Data Science […]
Read more
By their nature, data science products are risky. Building a Data Science MVP can reduce that risk by focusing the early development life cycle on discovery and learning. The onset of a data science project has a lot of unknowns – on the data, algorithm, systems, and business side. You […]
Read more
The machine learning process defines the flow of work that a data science team executes to create and deliver a machine learning model. In addition, the ML process also defines how the team works and collaborates together, to create the most useful predictive model. A High Level Machine Learning Process […]
Read more
A data science process can make or break a team. Indeed, we see time and time again that many of the reasons behind data science project failures are not technical in nature but rather stem from process-related issues. Simply throwing compute power and PhDs at the problem doesn’t work. Rather, […]
Read more
Explore what is a Machine Learning Life Cycle, and how it compares with a Data Science Life Cycle (by looking at OSEMN and CRISP-DM)…
Read more
The SAS Institute developed SEMMA as the process of data mining. It has five steps (Sample, Explore, Modify, Model, and Assess), earning the acronym of SEMMA. You can use the SEMMA data mining methodology to solve a wide range of business problems, including fraud identification, customer retention and turnover, database […]
Read moreWhat Is the KDD Process? Dating back to 1989, the namesake Knowledge Discovery in Database (KDD) represents the overall process of collecting data and methodically refining it. The KDD Process is a classic data science life cycle that aspires to purge the ‘noise’ (useless, tangential outliers) while establishing a phased […]
Read more
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 […]
Read more
During the past few months, we conducted a poll to see what project management framework teams used to help execute their data science projects. Based on our survey of 109 respondents, nearly half of the respondents most commonly use CRISP-DM. This was followed by Scrum, Kanban and “My Own”. See results below. […]
Read more