Analyzing Business Requirements for Data Science
This course takes you through the complete lifecycle of a data science project from proposal right through to the approval stage. It provides you with some invaluable insights on stakeholder management during this journey.
What you'll learn
Data Science has certainly become a ‘hot topic’ for all businesses. Investing in Data Science activities for your business can certainly yield some hidden gems in your data sets and lead to valuable IP or improvements in operational efficiencies for your organization! In this course, Analyzing Business Requirements for Data Science, you will learn the strategic, practical and technical skills to discover if data science can indeed benefit your organization. First, you will see how to gather and manage the right stakeholders with a view to providing a solid Data Science story to your company that adds REAL business value. Next, you will discover how to determine and risk assess your data science business and technical requirements. Finally, you will explore how to quantify the business problems and learn what to do when mitigating the risk of a project not yielding business value. When you're finished with this course, you will have the skills and knowledge of Analyzing business requirements for data science needed to guide you through the process of managing and delivering a data science project to your organization
Table of contents
- What Is Data Science? 2m
- What Is the Project Motivation Factor? 2m
- Hard-skills and Soft-skills 1m
- Identifying the Hard-skills 3m
- Meet the Stereotypical Technical Players 2m
- Identify Your Stakeholders 2m
- Selecting the Right Stakeholders 2m
- Get to Know Your Stakeholders 1m
- Managing the Initial Stakeholder Engagement 1m
- Asking the Right Questions 2m
- Capturing Stakeholders Needs 1m
- Assessing Stakehoders Needs 2m
- Accessing the Data 2m
- Project Gap Analysis 2m
- Present the Proposal 1m
- Accessing the Team's Reaction 1m
- Address and Capture Any Concerns 1m
- Establishing Agreement to Proceed the Project Further 1m
- Access the Need of the Project to the Business 1m
- High Risk Data Science Project Scope 1m
- Low Risk Data Science Project Scope 1m
- External vs. Internal Facing Project Scope 1m
- Defining Business Metrics 4m
- Business Metrics Classifications 3m
- Managing Technical Metrics 3m
- Data Science Project Risks 3m
- Data Science Project Lifecycle 2m
- Quantifying the Risks for the Data Science Project 4m
About the author
Paul is a practical and experienced software developer and architect. He leads in a practical hands-on manner and loves to share his experiences with the broader community. Paul has worked across a broad range of business sectors. He also has been involved in a number of successful startups solving complex software and hardware issues. He runs the Dublin Big Data and IoT meetup. He also has spent half of his career in IoT hardware design. Currently Paul is running his own business consulting wit... more