Bring Regression Models into the Real World
You have learned regression models—but what do you do on a real project? How do you answer when asked "Why did you choose this model?" in an interview? Does the "ML project" line on your CV actually stand out?
In this 3-hour advanced workshop, you will apply regression models to messy real-world business data, learn how to communicate model results to non-technical stakeholders, and get ready for data science interviews.
Enriched with real industry experience from banking and e-commerce—spanning real-world case studies, interview simulations, CV strategies, and portfolio building—your transition from theory to career starts here.
Instructors:
Who Should Attend
Those with basic regression knowledge – Learners who have completed introductory training and now want to see how models are applied in real projects.
Candidates preparing for interviews – Anyone aiming to give strong, structured answers to regression and case study questions in Data Scientist, ML Engineer, or Data Analyst interviews.
Junior data professionals – Practitioners in their first 1–2 years looking to develop production-grade thinking and workflows.
Those looking to strengthen their portfolio – Candidates who want to make their CV and GitHub projects stand out and tell a compelling story.
Analysts who present to business stakeholders – Professionals aiming to translate model outputs into business language for decision-makers.
Curriculum
Regression in the Real World
Textbook data vs. real-world data
Working with messy data: Missing values, outliers, inconsistencies
Feature engineering: Creating features with domain knowledge
End-to-end implementation on a real-world case study
Model Selection and Interpretation
Answering "Why did I choose this model?"
Model comparison strategies (selecting metrics in a business context)
Model interpretability: Making predictions explainable
Presenting to non-technical stakeholders: Translating R² into business impact
Regression in Interviews
Most frequently asked technical interview questions and ideal answer frameworks
Case study questions: "Given this dataset, how would you approach the problem?"
Live coding challenge format and practice
Behavioral questions: "Tell me about an ML project you built" (STAR method)
CV, Portfolio, and Career Strategy
How to write a Data Science CV (impact-focused bullet points)
GitHub portfolio: Which projects stand out and how to write a great README
Strategy for candidates without formal experience: Project = Experience
Landing a junior role and setting realistic expectations
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