Prerequisite: Attendees will need a laptop computer with specific software installed prior to the session. In advance of the class, they will receive detailed instructions for software download and installation.
This three-day set of intensive workshops covers what every business person needs to know about using data to make decisions and developing the fundamental data literacy skills required for the data-centric organization.
The curriculum is designed specifically for business users and does not require any previous background in analytics. Concepts are reinforced through a series of hands-on exercises that require no specialized software other than Microsoft Excel. Students learn similar techniques to those used by data scientists, but without programming.
Over three days of workshops, students will develop their data literacy vocabulary, sharpen their critical thinking skills, and learn how to use data to solve business problems. Techniques and best practices cover every stage of data-driven decision making. Students will learn to frame business problems, how to find the right data for the problem, how to develop analytic models, and how to assess results. Students will also learn how to present their findings for maximum business impact and how to critically evaluate the findings of others.
The final day of the program translates these skills into the world of machine learning using R. Through lessons designed specifically for people familiar with Excel, students will learn how to use the R environment for more robust models, including decision trees, random forest techniques, and more.
Your Team Will Learn
- Basic data concepts: summaries and visualizations
- Why business data often requires a different style of analysis
- Data analysis fundamentals
- The process behavior chart
- How to build process behavior charts in Excel
- How to rigorously identify trends/patterns in business data
- How to rigorously compare groups/collections of business data
- How to communicate your insights effectively
- The types of business problems where linear regression can be useful
- Why the arithmetic mean (a.k.a., the average) is a predictive model
- How simple linear regression improves upon the arithmetic mean
- How to use Excel to train simple linear regression models
- Ways to use Excel to train multiple linear regression models
- How to interpret linear regression models in terms of business drivers
- Ways to evaluate the effectiveness of your linear regression models
- Machine learning to solve classification problems
- The decision tree algorithm – including pros/cons
- The mighty random forest algorithm and how it "fixes" decision trees
- Testing your random forest for accuracy
- Data analysis and feature engineering using random forests
- Analyzing your feature engineering efforts – did they work?
- Additional resources to extend your learning
- Business analysts
- Knowledge workers
- Database developers
- BI and analytics developers
- Anyone who wants to move the needle on the business using data
No skills in programming or statistics are required.