Statistical Data Analysis Methods (Intermediate)

This course focuses on equipping participants with an advanced understanding of multivariate statistical approaches tailored for in-depth analysis and interpretation of complex data within deep tech fields. Upon successfully completing this intermediate-level course, participants will be prepared to make a substantial impact in the ever-changing field of deep tech. Armed with these intermediate-level abilities, they will be ready to engage in groundbreaking research and innovation, driving progress in the dynamic landscape of deep tech.

Designed for professionals navigating data-rich domains such as healthcare, finance, social sciences, and marketing, this intermediate-level course delves into advanced multivariate techniques essential for thriving in complex analytical landscapes. Participants elevate their statistical proficiency by focusing on a range of sophisticated methodologies, 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). These techniques enable professionals to uncover hidden patterns and insights within intricate datasets, empowering them to gain a competitive edge and make informed decisions in their respective fields. With an emphasis on practical application, this course equips participants with the tools and knowledge necessary to tackle the multifaceted challenges inherent in data-driven industries and drive impactful outcomes.