A two‑day intensive SPSS Workshop held on 16th and 17th March 2026 offered participants a deep dive into statistical analysis, data pre‑processing, and foundational machine learning techniques. The workshop was conducted across two venues:
- Day 1: Indian Institute of Hotel Management, Bangalore
- Day 2: PES University, Bengaluru
Organized under the guidance of Prof. Jaykumar and delivered by Dr. Thashneem T. Bhanu, the event brought together faculty members, postgraduate students, and a small group of six attendees who benefitted from hands‑on learning and practical demonstrations.
A Comprehensive Two-Day Learning Experience
Day 1: Statistical Modelling & Validation
- The first day focused on core statistical concepts using SPSS, including:
- Multiple linear regression
- Residual analysis and model validation
- Log and power transformations
- Polynomial regression models
- Multiple regression model building
- ANOVA (Analysis of Variance)
- Post-hoc testing
Participants gained practical exposure to diagnosing model issues, improving accuracy, and ensuring statistical reliability through validation techniques.
Day 2: Linking Statistics With Machine Learning
The second day expanded into broader analytical frameworks:
- Relationship between t-test and ANOVA
- Chi-square test for goodness of fit
- Chi-square test for independence
- Data pre‑processing methods
- Association Rule Mining
- Introduction to Classification
- Decision Tree
- Bayes Classifier
- K‑Nearest Neighbour
- Support Vector Machine
- Kernel Machine
- Clustering techniques
This progression from classical statistics to machine learning provided participants with a holistic understanding of analytical workflows.
Engaging Sessions and Knowledge Sharing
The event was anchored by Dr. Thashneem T. Bhanu, whose structured delivery helped simplify complex statistical concepts. The workshop also saw active involvement from IHM Bangalore faculty and postgraduate attendees.
Mr. Ankush Singh, Lecturer at IHM Bangalore, delivered the welcome and closing segments of the event.
Hands-On Highlights
The workshop’s standout features included:
Practical model validation techniques
Understanding transformations for linearity and variance stabilization
Bridging conceptual gaps between t-tests and ANOVA
Real-world application of chi-square tests
Hands-on experience with classification and clustering algorithms
Attendees left with enhanced analytical confidence and applicable skills for academic and professional research.
Key Learnings for Participants
By the end of the workshop, participants were able to:
Build and interpret multiple linear regression models
Validate models using residual analysis and normality checks
Apply transformations such as log and power functions
Perform ANOVA and related post‑hoc tests
Understand the relationship between statistical tests
Conduct chi‑square tests for various scenarios
Gain foundational exposure to machine learning models including decision trees, SVM, KNN, and Bayes classifiers
A Focus on Practical, Real-World Analytics
With a blend of statistical rigor and machine learning introductions, the workshop successfully equipped attendees with tools essential for modern data analysis. The combination of theory, demonstrations, and hands‑on practice ensured that learners could directly apply these techniques in their academic research or professional data projects.
