Introduction to Data Analysis

About this course

Learning objectives

After completing this course you should be able to:

1. Describe the process of creating an analysis plan, and give examples of alternative analytic purposes (e.g., explanatory versus confirmatory).
2. Describe the key data sources.
3. Name and define the key data types (nominal, ordinal, interval, ratio, etc.).
4. Explain the process of matching analytic techniques to different situations and needs, and give examples.
5. Summarize descriptive and visual approaches used to familiarize oneself with the data and to identify problems with the data.
6. Explain how to assess the impact of missing responses, and select and apply appropriate remedies.
7. State the reasons for and methods of statistically adjusting data; e.g., weighting, variable re-specification, and scale transformation.
8. Assess the characteristics of the distribution of the data and explain the implications of normality, non-normality, skewness, and multimodal data.
9. Illustrate the process for creating and testing hypotheses.
10. Compare and contrast the differences between type I and type II errors, and their potential impact on business decisions.
11. Describe the difference between statistical and business significance in the context of group comparisons, and explain the factors that have an impact on statistical significance.
12. Describe the difference between association and causality, and the potential impact on business decisions and outcomes.
13. Identify the major computer programs in current use in market research for the analysis of data.
14. Explain how to turn findings into market research conclusions, link findings to business decisions, and create actionable recommendations.

Who should attend?

• Entry-level researchers looking for a solid introduction to quantitative data analysis.
• Mid-level staff seeking to expand their skillset.
• Experienced researchers looking to catch up with the latest developments.
• Corporations seeking professional development options for their internal training portfolio.
• Suppliers seeking courses for new-employee onboarding.
• Researchers whose job involves leading or contributing to project design.
• Analysts needing to understand how best to analyze quantitative data, and the pitfalls to avoid.
• Client-side researchers responsible for designing research and ensuring that the analysis leads to reliable insights.
• People just entering the research field who want to understand this important aspect of the research process.

Continuing Education Information

Students successfully completing graded components earn a Digital Badge (Opens in a new window) and 1.2 University of Georgia Continuing Education Unit (Opens in a new window) (CEU) from The University of Georgia.

As a graduate of the course you will be recognized by industry associations, employers, peer groups and other professionals as understanding how to translate your research findings into reports and presentations that grab your audience’s attention, address the business decision your client needs to make, and offer sound and useful recommendations. This recognition will help you advance in your company and the industry.

CAIP Canada (Opens in a new window) also recommends the course for candidates looking to fill in the gaps or gain a refresher in specific areas.