Advanced Analytic Techniques

About this course

Learning objectives

After completing this course you should be able to:

1. Describe a common framework that distinguishes between multivariate analytic techniques and helps guide the decision of what technique to use when, based on the following factors—dependence, interdependence, number of dependent variables, type of relationship, item being analyzed, nature of metric, and the nature of the business question being addressed.
2. Compare and contrast the different patterns that express the relationship between two variables (e.g., nonlinear, linear, curvilinear, s-shaped, etc.).
3. Distinguish between interpolation and extrapolation.
4. Describe what Factor Analysis is, what it does, what type of input data is generally acceptable, and common applications in market research.
5. Describe the concept of Segmentation Analysis, what it does, what type of input data is generally acceptable, various techniques on how one may cluster data (e.g., K-Means, RFM, Pareto, etc.) and common segmentation applications in market research.
6. Describe what Perceptual Mapping (including the use of Multidimensional Scaling) is and common applications in market research.
7. Describe the different techniques used to measure association (i.e., Correlation, Simple Regression, and Multiple Regression), what they do, what type of input data is generally acceptable, and common applications in market research.
8. Describe Conjoint Analysis and Choice Modeling, what they do, what type of input data is generally acceptable, and common applications in market research.
9. Describe more advanced measures of association (e.g., Logistical Regression and Structural Equation Modeling), what they do, what type of input data is generally acceptable, and common applications in market research.
10. Describe what Discriminant Analysis is, what it does, what type of input data is generally acceptable, and common applications in market research.
11. Identify the most popular machine learning techniques and describe how researchers can use them to generate insight.
12. Describe what neural network analysis is, what it does, what type of input data is generally acceptable. Describe common applications in market research.
13. Describe the concept of Marketing Mix Modeling, what it does, what type of input data is generally acceptable, techniques that are used (e.g., multiple regression, Bayesian regression, etc.) and common applications in market research.
14. Describe Time Series Analysis, what it does, what type of input data is generally acceptable, what techniques are used, and common applications in market research.
15. Describe the difference between statistical significance and business significance.

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, particularly those around secondary data.
• 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.

Knowledge gained by this course

You should be familiar with:

1. The differences, strengths and limitations between primary and secondary data sources.
2. An elementary understanding and exposure to statistical analysis.
3. The ability to detect outlier observations.
4. How to frame business problems and suggestions around data which might assist in solving such problems.

These topics are covered in detail in two separate Principles Express courses. A more thorough overview of some of these techniques can be found in: Introduction to Data Analysis and Working with Secondary Data: Syndicated and Big Data. See Principles Express (Opens in a new window) courses for more details.