Data Discovery to Data Wisdom: A Hands-on Analytics Class
Learn a framework for understanding data as a resource, analytics as a tool, and intent as the true challenge. Explore industry vocabulary, common pitfalls of data visualization and uses (as well as misuses) of complex statistical modeling. Whether you come from a landscape with too much data and no idea what to do with it; or no data collection practices in place, you can leave with a plan.
Gain an understanding of basic data analysis and modeling principles to inform your day-to-day roles and place you (and your team or organization) ahead of the competition. The workshop instruction cuts through the noise and gives practical and simple rules for the journey from data discovery to data wisdom.
This class is taught in a computer lab, using case studies and data sets to practice with the analytics methods discussed. Excel Level 1 and 2 or equivalent required.
Brendon Dorn performs research, simulation and analysis of various energy and functional strategies at HGA to identify and quantify their impact on the overall project. Dorn’s efforts are used to provide successful, cost-effective and energy-efficient outcomes to assist in savings and ... read more
Who Should Attend
Business analysts, financial analysts, project managers
Benefits and Learning Outcomes
- Understand intent of data analysis and how to form it (or demand it)
- Learn who owns the organizational data you need and how to access it
- Develop a radar for pitfalls and misuses of data analysis to keep your organization clear of danger
- Learn how to build a case or value proposition for acquiring absent data that would be valuable
- Create an engaging interpretation of analysis results with a focus on your business’s context
1) Data as a resource
-history of data utilization
-creating a collection strategy
-heuristics for using the resource wisely
2) Visualization: data to information
-history of data visualization
-best and worst practices (Edward Tufte, David McCandles)
3) Predictive and Explanatory Modeling
-basic statistics and forecasting
-modeling vs reporting and its benefits
-business uses and abuses of models
-balancing predictive edge with risk
4) Decision Support
-analytic’s scientific method life cycle
-integrating data strategy, visual tools and predictive models
-listening to models to find what data we DON’T have but should
-outcome categorization; “replicate or avoid”
We will also survey 10 data visualization tools, demonstrate a basic regression model and test some intuitions that surface in data exploration.
-get to know a dataset: things to check, questions to ask
-visualize your data: create performance indicators, comparisons and some quick statistical tools
-predictive modeling in twenty: choose the variable we want to predict, build and refine the model and make predictions!
-interpret errors and other model parameters that will keep you safe in understanding predictions
Excel 1 and 2 or equivalent
"Fantastic and enthusiastic instructor!" — Heather Hansen, DLSS
"The content was very interesting and I appreciated the different ways to analyze and interpret data." — Sakura Gamblin