Toolkit for SAP consultants

Good Product Backlog Definition Aspects of a product backlog Product Backlog Structure and User Story Mapping Story-driven communication Document-driven communication Preconditions for User Story Mapping (USM) Product vision statement Creating As-Is and To-Be Processes Creating the Walking Skeleton User story mapping Writing, Refining, and Splitting Your User Stories INVEST criteria Deriving Non-Functional Requirements from Your User Stories Product Backlog Validation and Refinement A Deep Dive into the Persona Technique Validating Your Backlog with Storyboards Building Your Storyboard with SAP Scenes on MURAL Collaborating with Your Customers During Discovery Project pre-sprint activities Refining Your User Stories with Customers Product Backlog Ranking Estimating Complexity for Your User Stories Identifying the Business Value Categorizing User Benefits with the Kano Methodology Ranking Your Product Backlog Planning the Delivery of Product Increments Good Product Backlog Definition As

Selection via join and filling two virtual columns as select ekpo.matnr as Material, ekpo.werks as Plant, ekpo.meins as UnitOfOrder, case ekpo.pstyp when '2' then 0 else case ekpo.retpo when 'X' then -1 * eket.menge - eket.wemng else eket.menge - eket.wemng end end as open_po_qty, case ekpo.pstyp when '2' then case ekpo.retpo when 'X' then -1 * eket.menge - eket.wemng else eket.menge - eket.wemng end else 0 end as open_po_consi_qty, ekpo.umrez, ekpo.umren from ekpo as ekpo inner join eket as eket on ekpo.mandt = eket.mandt and ekpo.ebeln = eket.ebeln and ekpo.ebelp = eket.ebelp inner join ekko on ekpo.mandt = ekko.mandt and ekpo.ebeln = ekko.ebeln where eket.wemng < eket.menge and ekpo.loekz =

  References:  Data preparation  Introduction SAP HANA Predictive Analysis Library Association Analysis Classification Analysis Regression Cluster Analysis Time Series Analysis Probability Distribution Outlier Detection Link Prediction Data Preparation Statistic Functions (Univariate) Statistic Functions (Multivariate) Introduction to Project Methodologies Phase 1.1: Determine Business Objectives ▪ Task − The first objective of the data analyst is to thoroughly understand, from a business perspective, what the client really wants to accomplish. Phase 1.2: Assess Situation ▪ Task − In the previous task, your objective is to quickly get to the crux of the situation. Here, you want to flesh out the details. ▪ Outputs − Inventory of resources − Requirements, assumptions, and constraints − Risks and contingencies − Terminology − Costs and benefits Phase 1.3: Determine Data Science Goals ▪ Task − A business goal states objectives in business terminology. − A data science goal states project