Statistical Data Analysis Methods (Intermediate)

  • Course level: Intermediate  
There are no active Semester Schedule for this course   Pre-registar


This intermediate-level course empowers professionals with advanced multivariate techniques crucial for thriving in data-rich fields like healthcare, finance, social sciences, and marketing. Elevate your statistical proficiency with a focus on techniques from Multivariate Analysis of Variance (MANOVA) to Principal Component Analysis (PCA), uncovering hidden patterns and insights in complex datasets for a competitive edge in today's analytical landscape.


  • Master Advanced Multivariate Techniques:

Build upon fundamental data knowledge by acquiring a comprehensive understanding of advanced multivariate techniques, including Multivariate Analysis of Variance (MANOVA), Analysis of Covariance, Repeated Measures ANOVA, Logistic Regression (Binary & Multinomial), Ordinal Regression, Cox Regression, Factor Analysis, and Principal Component Analysis (PCA).

  • Apply Techniques Across Diverse Fields:


Extend the ability to apply advanced multivariate techniques beyond basic statistical principles, addressing the complexities of healthcare, finance, social sciences, and marketing. Develop expertise in analyzing intricate data structures to inform decision-making.

  • Uncover Hidden Patterns and Relationships:

Enhance skills in identifying hidden patterns and relationships within complex datasets, building on the foundational knowledge of Descriptive Statistics and Hypothesis Testing. Utilize advanced techniques to extract nuanced insights essential for strategic decision-making.

  • Utilize Statistical Software Proficiently:

Extend proficiency in using statistical software tools to implement a broader range of multivariate analyses. Develop hands-on experience in applying logistic regression, ordinal regression, factor analysis, and PCA for practical insights.


  1. Multivariate Analysis of Variance (MANOVA)
  2. Analysis of Covariance
  3. Repeated Measures ANOVA
  4. Logistic Regression (Binary & Multinomial)
  5. Ordinal Regression
  6. Cox Regression
  7. Factor Analysis
  8. Principal Component Analysis (PCA)

Registration fee :
€ 1200

Target audience

  • Academicians
  • University Lecturers
  • Researchers
  • University Students
  • Statisticians


  • Recommended solid foundation in basic statistical concepts.
  • Familiarity with statistical software such as R, SAS, SPSS, etc., will enhance the learning experience.