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Digital Transformation with Excel

Digital Transformation
with Excel

J.D. Landgrebe, Course Author

President of DataDelve Engineer LLC Consulting; Formerly Principal Engineer, Procter and Gamble R&D

About this Course

In many businesses, important data exists in “scraps” of spreadsheets and important disconnected models. Sound familiar? 

Money is lost because the data is disconnected and unstructured, destined only to be used once.

This course is designed for many types of employees, from management and administrators, to statistical users. It will show you actionable, pragmatic methods for using Microsoft Excel to unify data across your organization, increasing your organizational efficiency.  

We put the material together based on a lot of consulting experience with diverse projects and teams – to help people master excel of course, but to convey bigger picture techniques for working with data and models. Excel is an ideal software vehicle for teaching such concepts. 

Our four-part course framework includes specific skills and techniques like: 

  1. How to be fluent in Excel to enable working well with large and small data sets and models (Example skill: Built-in, no memorization shortcut system)
  2. How to structure data for extensibility and portability (Example skill: Building multi-table models with lookup functions
  3. How to curate data to maximize personal and collaborator/customer useful life of data (Example skill: Cell and Range naming to curate calculation model formulas.
  4. Designing excellent user interfaces in Excel to encourage use of data and calculation models for collaboration and decision-making (Example skills: Column Outlines and Conditional Formatting techniques for working comfortably with large models) 

What you will learn

  • How to make data collaborative and portable across applications
  • How to build structured, curated models and business process applications
  • Personal, data and modeling skills
  • How to encourage digital transformation culture
  • How to better curate your personal work
  • Skills for engaging internal customers
  • Skills for creating order with your organization’s business data
  • Skills for engaging internal customers with data products they can pick up and use for their work
  • Insights on when to use Excel versus non-Microsoft tools

Who Should Attend

Functional experts, Managers and Leaders, Modeling, statistics and data science experts, Administrators, Analysts.

Duration
6 hours (with breaks)

Prerequisites

Previous, basic familiarity with Microsoft Excel on an accessible laptop

Desktop Mac or Windows Excel installed on computer (not online/Sharepoint/Teams version!)

Pricing
Individual and corporate pricing available on request

Instructors

Instructor

J.D. Landgrebe

Specialist in data analytics, empirical modeling and optimization.

President of DataDelve Engineer LLC Consulting; Formerly Principal Engineer, Procter and Gamble R&D

Course Syllabus

6 Hours

Course Theme Syllabus Topic Description
Big Picture Intro course and Instructor
Fluency Excel Fluency Intro
Fluency Issuing commands efficiently
Structuring Definition of “structured” rows/columns for data and models
Structuring Making a pivot table from structured data
Structuring Structured Scenario Models –an alternate structured template for calculation models
Fluency Navigating within and between workbooks
Fluency Navigating dialog boxes
Fluency Selecting data efficiently
Curating Curating data and models
Commands Intro to Trainings on Excel Commands: Cost Model and Roll Length Model overviews and exploration
Commands Skill modules for curating variables and models: Find/Replace, Filtering, Sorting, Naming Ranges
Commands Named Cells
Commands Naming Ranges
Commands Find and Replace
Aesthetics – UI Skill modules for Calculation Model User Interface Design: Arranging columns by granularity, Column Outlines, Freeze Panes, Conditional Formatting
Commands The VLOOKUP/MATCH combo for multi-table models
Big Picture Wrap-up

Knowledge you can put to work

Register for "Mixture Experiments Using Machine Learning: 'A How-to Approach'"