Why Attend this Course?

  • What can I do as a business analyst and when do I need to start learning specialized data analytics skills?
  • This course examines several depictions of data analytics and defines boundaries that can define the need for specialized skills
  • 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.
  • Recommendations are typically buried in misunderstood charts and tables upon tables of data.
  • This course introduces four principles for data visualizations – graphics that help find the unexpected and communicate data as insights.

What Makes this Course Stand Apart?

  • The course is constructed around common data analytics competencies.
  • A case study approach links together the various topics discussed.
  • Hands-on exercises challenge participants with issues faced during typical analytics projects.
  • Covers the very latest ‘thinking’ in data analytics.

What You Will Learn

Upon completion of this course you will be able to:

  • Distinguish between different types of data analytics projects.
  • Understand the key challenges of data analytics projects.
  • Define the key attributes of companies that compete on analytics.
  • Use a portfolio of examples to recognize opportunities within your company.
  • Critique visualizations as a means of improving their communication abilities.


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


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


1 day

Outline for Fundamentals of Data Analytics

1. Big Data

  • Big Data/Social Media Data/Industrial Data
  • Volume, Velocity, Variety & Variability 
  • Challenges & Opportunities with Big Data

2. Data Analytics

  • Types of Analytics Projects with real world examples
  • Common Competencies and Skills
  • A Process for Analytics
  • Common Challenges
  • Data Mining Techniques (An Overview)

3. Data Visualization

  • Pre-attentive Processing
  • Data Types and Visual Attributes
  • Critiquing Visualizations for Improvement