Data Science Lab

Abstract

The objective of this course is to gain hands-on experience of dealing with data science and machine learning applications “in the wild". Participants work in teams on projects and get go through the full process starting from data cleaning, modeling, execution, debugging, error analysis, and quality/performance refinement.

Learning Objectives

  • analyze, derive, and align project goals, expectations, objectives, and KPIs from stakeholder needs
  • apply adequate data cleaning and pre-processing techniques for a real-world problem, including handling missing data, labelling, outliers, feature engineering, scaling, and normalization
  • design, develop, and evaluate machine learning models together as a team
  • debug and tune the performance of the built model, including outlier analysis
  • communicate and present the results of machine learning projects effectively, both orally and in written reports, to technical and non-technical stakeholders
JavaScript has been disabled in your browser