Practicing Agile data science
Join expert Russell Jurney to explore and learn how to apply the Agile strategies outlined in his book, Agile Data Science 2.0. You'll learn about the history of Agile methods and discover how to set up, structure, and manage analytics projects. Along the way, Russell walks you through an overview of big data and the differences between software engineering and data science, which require changes in methods to make Agile methods effective. You'll leave with an understanding of the dynamic nature of data science, which is both engineering and science, and the skills to avoid common pitfalls and create an application for your problem domain that delivers consistent, predictable results.
What you'll learn-and how you can apply it
By the end of this live online course, you’ll understand:
- Agile methods, concepts, and context as they apply to software and data science
- How data science differs from software engineering
- How to apply Agile methods to data science
- How to align data science with the rest of the organization
- How to combat “the pull of the waterfall”
- How to leverage warehouse-scale computing for your organization
- How the data-value pyramid gives structure to data science
And you’ll be able to:
- Manage real data science progress rather than elusive end goals
- Turn applied research into rapid results
- Focus Agile processes on actionable insight rather than software features
- Handle the uncertainty data science brings compared to software engineering
- Deliver on the promise of data science for your organization
- Act on big data to exploit new opportunities
This training course is for you because...
- You're a member of a data science team (software engineer, data scientist, designer, product manager, project manager, etc.) and are interested in applying Agile methods to your data science projects.
- You're a product, project, or program manager looking for management training.
- You're an executive who wants to understand Agile methodologies for data science.
- Experience on a data science or software team (useful but not required)
- Familiarity with an Agile methodology (useful but not required)
"Theory" (chapter 1 in Agile Data Science 2.0)
About your instructor
Russell Jurney “wrote the book” on agile data science. He has been building full stack analytics applications for ten years. Russell is principal consultant at Data Syndrome, where he teaches agile data science and works with full stacks daily to build analytics products for paying customers.
The timeframes are only estimates and may vary according to how the class is progressing
The waterfall method (20 minutes)
- Lecture: Overview of software engineering from the birth of software to the waterfall method; the benefits and drawbacks of the waterfall method; the birth of Agile methods
The history of Agile (15 minutes)
- Lecture: The history of Agile methods
Waterfall method hands-on (20 minutes)
- Hands-on exercise: Use the waterfall method to translate a product specification from contract to working software
Break (10 minutes)
Agile software (20 minutes)
- Lecture: A survey of Agile methods—including Scrum, test-driven development, Kanban, and pair programming—focusing on the problems they address and how they do so
Case study on LinkedIn InMaps and the software-data science mismatch (15 minutes)
- Lecture: A case study on LinkedIn InMaps, LinkedIn’s first attempt to use Agile methods on an analytics product; how the challenges of big data and the "pull of the waterfall” resulted in problems
Big data: The opportunity (15 minutes)
- Lecture: The history of big data; its core opportunities; how to take advantage of them
Break (10 minutes)
Agile Data Science 2.0 (20 minutes)
- Lecture: The mechanics of Agile Data Science 2.0; how to think about analytics in an Agile way; a process where the output of data science becomes the focus of the application you build
John Akred on day-to-day operations (15 minutes)
- Lecture: John Akred's Agile methods—innovative ways of managing the day-to-day operations of an Agile data science team
Applying Agile data science (20 minutes)
- Hands-on exercise: Create a task list for members of a data science team during several project sprints