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AGILE TRANSFORMATION FOR DATA SCIENCE

Course outline

3 days
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Duration

3 days

Overview

Agile Transformation for Data Science is an approach to data science centered around web application development. It asserts that the most effective output of the data science process suitable for effecting change in an organization is the web application. It asserts that application development is a fundamental skill of a data scientist. Therefore, doing data science becomes about building applications that describe the applied research process: rapid prototyping, exploratory data analysis, interactive visualization, and applied machine learning.

The Agile Data Science “manifesto” is an attempt to create a rigorous method to apply agility to the practice of data science. These principles apply beyond data scientists building data products in production. The web application is the best format to share actionable insights both within and outside an organization.

Proper agile project planning and management is the foundation of agile analytics. Unlike conventional project management methods, agile requires a highly adaptive and evolutionary approach that delivers chunks of value to the business every few weeks for feedback and acceptance. As Dwight D. Eisenhower once said, "I have always found that plans are useless, but planning is indispensable." Agility calls for planning for what you know today and being prepared to adjust in the face of change and uncertainty.

Agile analytics planning is a highly collaborative process that includes long-range business intelligence road mapping to establish future vision, short-horizon planning of near-term delivery projects, and detailed iteration planning so the team will know how to get started. This course will walk you through typical collaborative BI program road mapping, project chartering, and planning sessions to introduce you to a set of effective practices for facilitating collaboration between technical team members, end users, and management stakeholders.

Benefits

  • Have Fully-Functional Teams: Staff the data science team with all the skillsets needed to deliver value
  • Allow Teams to Self-Manage: Upper-level management should not dictate how the team should function. Rather, they should provide direction and an environment for them to succeed. Encourage and trust the team to self-organize. The team should maintain a sustainable pace, frequently inspect its processes, and continually improve
  • Start Simple and Iterate Quickly: The primary output of data science teams are insights. Initial insights can start with static reports or analyses from data exploration. Then build up to interactive dashboards, Minimal Viable Models, and then to fully-functional productized intelligent systems
  • Measure, Measure, Measure: Solicit feedback regularly both from stakeholders (via demos) and from the data itself (by closely monitoring model performance)
  • Collaborate: Long gone are the days of the lone wolf data scientist hiding in a corner. Rather, data science is a team sport. Agile data science teams collaborate and frequently communicate among themselves and with the broader stakeholder team

COURSE OUTINE

DAY 1Module 1: Introduction to Agile Transformation for Data Science

  • Agile Data Science
  • Agile in Business Intelligence
  • Importance of Agile Data Science in Business Intelligence
  • Agile Data Science Methodology
  • Advantages
  • Disadvantages
  • Agile Data Science for business intelligence platforms

Module 2: Agile Team for Data Science in Business Intelligence

  • The Agile Data Science Team Structure
  • The Role of Developers
  • The Role of Business and Data Analysts
  • Scrum for Business Intelligence
  • Project Roles (including Scrum Master, Product Owner and the Team)

DAY 2Module 3: Agile Processes Applied on Data Science

  • Vision Board
  • About Product Backlogs
  • Prioritizing Product Backlogs
  • Writing User Stories
  • Product Release
  • Sprint Backlogs
  • Sprint Requirement
  • Sprint Planning
  • Sprint Review Meeting

Module 4: Agile Tools & Techniques in Data Science

  • Standardizing BI development tasks and templates
  • Use of prototyping as a way to clarify BI requirements
  • Test Driven Development (TDD)
  • Writing Unit Tests for Business Intelligence
  • Testing the ETL process
  • Testing data for appropriateness
  • Testing Report Output

Module 5: Continuous Integration and Improvement of Agile Data Science

  • Continuous Integration for Business Intelligence
  • Tracking and Reporting
  • Burndown Charts
  • Dealing with Scope Creep
  • Early-trend and Feedback Metrics
  • Reporting to “Customers”
  • Developing your Business Intelligence Capability
  • Running Your Day-to-Day Operations

Module 6: Retrospective, Analytics and Reporting in Agile Data Science

  • Business Intelligence Iterations
  • Agile Collaborative Workspace
  • About Agile Retrospective
  • Inspect & Adapt Retrospective
  • Leading Agile Retrospective
  • Releases & Project Retrospective

NoteAll sessions contain group activities and case studies to strengthen the understanding of Agile Transformation for Data Science to be applied at respective individual’s organization.

Programme scope, delivery arrangements and suitability are confirmed during enquiry.