Project Management for Data Analysts
£ 299.00
In this course, you’ll learn the concepts and tools most commonly used for project management in data science. This course is for anyone who is interested in learning the approaches and techniques used for project management in data science and other technical fields.
Data analysts must juggle being technically sound, being thoughtful business partners, having strong project management skills, and remaining focused on the priorities of their organisation.
Knowing which tools to use –– and when –– is a key part of the data analyst’s planning and decision making.
Just as this is the case with the technology tools the data analyst uses, so is it true of the project management processes and systems that will be deployed for any given project.
This course will introduce you to the essential tools and techniques used for project management and collaboration in data science and other technical fields.
You’ll explore the essential concepts of Lean, Agile, and Scrum methodologies and their application.
The course consists of 11 modules and includes demonstrations, hands-on activities, and knowledge tests at the end of each section of the course.
Module 1: Understanding the Collaboration Tools
Module 2: Software Project Management Communication Skills
Module 3: Lean, Agile, and Scrum Methodologies
Module 4: Using Lean to Perfect Organisational Processes
Module 5: Using Lean to Improve Flow and Pull
Module 6: Using Lean to Reduce Waste and Streamline Value Flow
Module 7: Applying Value Stream Mapping in Lean Business
Module 8: Agile Principles and Methodologies
Module 9: Agile Project Planning
Module 10: Agile Project Scheduling and Monitoring
Module 11: Agile Stakeholder Engagement and Team Development
Who is this course for?
The aim of the course is to ensure your understanding of the fundamental research methods and tools used to draw conclusions based on data.
Requirements
There are no prerequisities for this course.
Career path
To enter the field of data science, it’s essential to learn the techniques underpinning data research and statistics. A strong understanding of research methodology and modelling is key to performing robust analytics and data interpretation
Data analysts must juggle being technically sound, being thoughtful business partners, having strong project management skills, and remaining focused on the priorities of their organisation.
Knowing which tools to use –– and when –– is a key part of the data analyst’s planning and decision making.
Just as this is the case with the technology tools the data analyst uses, so is it true of the project management processes and systems that will be deployed for any given project.
This course will introduce you to the essential tools and techniques used for project management and collaboration in data science and other technical fields.
You’ll explore the essential concepts of Lean, Agile, and Scrum methodologies and their application.
The course consists of 11 modules and includes demonstrations, hands-on activities, and knowledge tests at the end of each section of the course.
Module 1: Understanding the Collaboration Tools
Module 2: Software Project Management Communication Skills
Module 3: Lean, Agile, and Scrum Methodologies
Module 4: Using Lean to Perfect Organisational Processes
Module 5: Using Lean to Improve Flow and Pull
Module 6: Using Lean to Reduce Waste and Streamline Value Flow
Module 7: Applying Value Stream Mapping in Lean Business
Module 8: Agile Principles and Methodologies
Module 9: Agile Project Planning
Module 10: Agile Project Scheduling and Monitoring
Module 11: Agile Stakeholder Engagement and Team Development
Who is this course for?
The aim of the course is to ensure your understanding of the fundamental research methods and tools used to draw conclusions based on data.
Requirements
There are no prerequisities for this course.
Career path
To enter the field of data science, it’s essential to learn the techniques underpinning data research and statistics. A strong understanding of research methodology and modelling is key to performing robust analytics and data interpretation
Category Data Analysis