Plan end to end data science projects including activities involved, dependencies, external/internal resource needs and skills requirements
Manage stakeholder expectations on the delivery of data science projects
Manage data science team and ensure alignment to larger project/program objectives
Plan communications on status reporting of data science projects with details of all activities
9 sections • 39 lectures • 1h 20m total length
Data Science Methodology - CRISP-DM
Determine Business Objectives
Determine Data Mining Goals
Produce Project Plan
Business Understanding Phase Conclusion
Business Understanding Quiz
Data Understanding Phase Introduction
Collect Initial Data
Verify Data Quality
Data Understanding Phase Conclusion
Data Understanding Quiz
Data Preparation Phase Conclusion
Data Preparation Quiz
Modeling Phase Introduction
Select Modeling Technique
Generate Test Design
Modeling Phase Conclusion
Evaluation Phase Introduction
Determine Next Steps
Evaluation Phase Conclusion
Deployment Phase Introduction
Plan Monitoring and Maintenance
Produce Final Report
Deployment Phase Conclusion
Deployment Phase Quiz
This course is to enable learners to successfully manage a data science project. It is process oriented and explains CRISP-DM methodology. CRISP-DM, stands for Cross Industry Standard Process for Data Mining, and it is the most widely used, holistic framework for data science projects.
This course takes you through the data mining activities in the context of Project Management. The project explains inputs and outputs of all activities helping effective project management of a data science project. As per the Project Management best practices it guides you to engage the right stakeholders to help setting Data Mining Success criteria to achieve the business goals.
Machine Learning and Model building activities using Python or R are an important activities in any data science project. However, there are several other activities that are part of any Data Science project. The data needs to be prepared for application of machine learning techniques. There are lot of steps involved in preparing a data-set which would be suitable for achieving the business goal of the data science project. In this course, we are going to take a broader look and identify how each activity of CRISP-DM fits together towards achieving business outcomes of a data science project.
The course lists the monitoring, reporting and user training needs during the execution of data science project. The data science project needs to conclude with deployment of data mining results and review of lessons learned. All the above activities are sequenced in this course along with their purpose and elaborate details for end to end execution of a data science project.
Who this course is for:
Project/Program Managers who want to manage data science, Artificial Intelligence and Machine Learning projects
Business Analysts/ Domain Specialists/ Business sponsors who need to drive business improvements through data science projects
Data Science team members (developers/technologists/data analysts) who want to get the big picture of data science projects
Aspiring Data science professionals, who want to learn complete methodology without having to learn coding or technical topics
We are a group of highly experienced Hi Tech Industry professionals who are passionate about sharing their knowledge and expertise in their respective domains. We all have years of experience in various technical and business topics in MNCs and want to use e-learning platforms to help students gain success from our experience.