Why Attend this Course?

  • Many analysts start their analysis by opening EXCEL. 
  • There is a process that should be followed for data analytics projects.  This will be introduced and utilized during class.
  • Decisions are made based on the analysis without knowing how the underlying data looks.
  • Exploratory data analysis emphasizes ‘knowing the data’ as a precursor to decision-making.  This course will introduce an organized and structured approach to looking at variable distributions, missing data, outliers and other typical data issues.
  • A common mis-conception is that more data is better.  The truth is better data is better.  Technology allows us to create models more easily and quickly than in the past which is often an impetus to ‘just add more variables’.
  • Continuing with the line of organized and structured, this course will present techniques to determine the relationships among explanatory variables and assess the relationships between explanatory and outcome variables.

What Makes this Course Stand Apart?

  • An emphasis on utilizing the abundance of data in an organized, structured and well thought out manner.
  • A strong focus utilizing data visualization techniques that highlight the unexpected.
  • A case study approach using real world data.

What You Will Learn

  • Upon completion of this course you will be able to:
  • Detect mistakes within the data
  • Validate assumptions
  • Determine relationships among the explanatory variables
  • Assess the relationships between explanatory and outcome variables


  • Business Analyst (IT and non-IT)
  • Data Quality Analyst
  • Project Leaders
  • Systems Analyst
  • Data Analyst


The structure of the courses assumes no prior experience in statistics or data analytics. 


1 day

Outline for Exploratory Data Analysis

1. Data Analytics

  • Defining the Analytic Question
  • Making it Measureable
  • Planning the Analysis
  • Identifying Useful Variables
  • Collecting and Managing Data
  • Integration
  • Conducting the Analysis
  • Exploratory Analysis
  • Five Number Summary
  • Outliers
  • Missing Data
  • Reducing the Number of Variables
  • Correlations
  • Making Recommendations Based on Data
  • Deploying and Monitoring Recommendations