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Deliver better outcomes with the most comprehensive set of resources dedicated to data science project management.| Data Science PM
Deliver better outcomes with the most comprehensive set of resources dedicated to data science project management.| Data Science PM
An effective project manager in data science understands how to apply appropriate project management practices to data science projects.| Data Science PM
Building a Data Science MVP (Minimal Viable Product) step in your project plan can shorten time to initial delivery and decrease risk.| Data Science PM
A Data Science Life Cycle describes the steps to deliver projects and products. This post walks through one you can use on your next project.| Data Science PM
An effective data science process outlines both the project steps and how the team works together to execute these steps.| Data Science PM
A data science workflow defines the phases (or steps) that the team should execute to successfully deliver a project.| Data Science PM
Waterfall project management plans set a rigid plan upfront. This inflexibility creates challenges for data science projects.| Data Science PM
How do you prioritize deliverables in a data science project? The best was to deliver value is by slicing your project into end-to-end value pieces.| Data Science PM
Existing agile frameworks don't always work for data science. Therefore, learn about Data Driven Scrum - a new framework specific for data science teams.| Data Science PM
Data science projects are challenging. To get started on the right foot, ask yourself these 10 data science questions.| Data Science PM
What can you learn from observing 20 data science teams. Dive into some of the best practices that you can apply on your next project.| Data Science PM
How can you effectively lead data science teams? The answer isn't straight-forward but these 8 tips can get you started.| Data Science PM
Data science vs software engineering? Both are great fields that are similar in many ways but there are distinct differences.| Data Science PM
What's the difference between the data analyst vs data scientist roles? While similar, they each play their distinct role in a team.| Data Science PM
How should you organize your data science team structure? Learn the pros and cons of centralized vs decentralized teams.| Data Science PM
What is a Data Product Manager?| Data Science PM
Ethical issues in data science are perplexing. How should you incorporate ethical decision-making into your team and projects?| Data Science PM
As the data science function matures, a dedicated product manager to drive product vision and strategy is becoming increasingly crucial.| Data Science PM
What is Agile and is it a fit for data science? Generally, yes but the agile methodologies need to flex to the data science's unique needs.| Data Science PM
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Microsoft's Team Data Science Process (TDSP) combines elements of Agile, modern software practices, and CRISP-DM. Is it effective for data science projects?| Data Science Process Alliance
SEMMA is a step-by-step data mining process used to extract knowledge in databases. Learn about how to use it your data science projects.| Data Science Process Alliance
Does Scrum work for data science projects? Software's most popular agile framework provides some great benefits but it also constrains data science work.| Data Science Process Alliance
What is the Knowledge Discovery in Databases (KDD) Process and Data Mining? Learn how to use these in your data science projects.| Data Science Process Alliance
What is Kanban, and does it work for data science? Its flexibility is ideal for many projects, espicially when combined with more comprehensive approaches.| Data Science Process Alliance
This is the web's most comprehensive guide to managing data science projects. Combine a data science methodology with an agile approach.| Data Science Process Alliance
CRISP DM is the most popular framework for delivering data science projects. Learn 5 keys to leverage CRISP DM on your projects.| Data Science Process Alliance
The CRoss Industry Standard Process for Data Mining (CRISP-DM) is a process model with six phases that describes the data science life cycle.| Data Science Process Alliance
Data science is a team sport. These eight key data science team roles can help ensure you can successfully deliver projects.| Data Science Process Alliance
Based on a recent poll conducted on our site, CRISP-DM remains as the most popular framework for data science projects.| Data Science PM
85% of data science projects fail. Why? Learn these eight leading reasons and what you can do to beat the odds.| Data Science PM
A data product owner is an exciting and crucial role that bridges the gap between data and business strategy.| Data Science Process Alliance
AI Program Manager serve as the crucial link between AI technologies, business objectives, and project execution.| Data Science Process Alliance
The GenAI life cycle delineates the steps for creating AI-based applications, such as chatbots, virtual assistants or intelligent agents.| Data Science Process Alliance
The AI product owner is an emerging, in-demand role. Scrum and Data Driven Scrum teams use this role to define and drive product development.| Data Science PM
In today's data-driven world, an Analytics Product Manager plays a pivotal role in bridging the gap between data and business outcomes.| Data Science Process Alliance
Learn what are the key differences and similarities of data analytics vs data science, and how that impacts how they are managed.| Data Science Process Alliance
Gathering and defining data science project deliverables is challenging. User stories are a great format to help bridges the challenges.| Data Science Process Alliance
To get the most out of investments in AI, understand: what is Agile, what is AI, and implement 7 practical Agile AI tips.| Data Science Process Alliance
Explore a project life cycle to enable you to managing generative AI projects that create chatbots and other agents .| Data Science Process Alliance
Companies are looking for AI Product Managers who manage business, technology, and data to develop, launch, and operate AI products.| Data Science PM
Ensuring Responsible AI is a key part of data science project management and machine learning model building| Data Science PM
5 questions to evaluate a team's process. Explore if an existing Analytics / Data Science maturity model can help evaluate a team's process| Data Science PM
