Data Analysis Boot Camp

This three-day course, organized into key topic areas, leverages straightforward business examples to explain practical techniques for understanding and reviewing data quality. You will learn how to make more informed, intelligent business decisions by analyzing data using Excel functions and the R programming language.

You will get an overview of data quality and data management, followed by foundational analysis and statistical techniques. Throughout the course, you will learn to communicate about data and findings to stakeholders who need to quickly make decisions that drive your organization forward.

In–Class Exercises, Demos, and Real-World Case Studies

This data analyst training class is a lively blend of expert instruction combined with hands-on exercises so you can practice new skills. Leave prepared to start performing practical analysis techniques the moment you return to work. Every Data Analysis Boot Camp instructor is a veteran consultant and data guru who will guide you through effective best practices and easily accessible technologies for working with your data. Through a combination of demonstrations and hands-on practice, you will learn to use data analysis techniques, which are typically the domain of expensive consultants.

Labs for this course are primarily in Microsoft Excel, however, students will get an opportunity to practice using R in some labs. Labs for this course can also be taught using the Python programming language for private onsite clients only.

In This Data Analysis Training Course, You Will: 

• Identify opportunities, manage change and develop deep visibility into your organization
• Understand the terminology and jargon of analytics, business intelligence, and statistics
• Learn a wealth of practical applications for applying data analysis capability
• Visualize both data and the results of your analysis for straightforward graphical presentation to stakeholders
• Learn to estimate more accurately than ever, while accounting for variance, error, and Confidence Intervals
• Practice creating a valuable array of plots and charts to reveal hidden trends and patterns in your data
• Differentiate between "signal" and "noise" in your data
• Understand and leverage different distribution models, and how each applies in the real world
• Form and test hypotheses – use multiple methods to define and interpret useful predictions
• Learn about statistical inference and drawing conclusions about the population