Business Analytics and Data Science

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Business Analytics and Data Science

Introduction to the foundations of Data Science with focus on business applications. We emphasize supervised machine learning algorithms to build predictive decision support models for credit risk, marketing analytics, and several other use cases.

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  • 0 Reviews
  • 24 Students Enrolled
  • Free
  • Course Includes
  • GitHub Repository
Tags:
MachineLearning PredictiveAnalytics Python



Courselet Content

6 courselets • 26 courselet components • 10h 25m total length
45min
30min
Jupyter notebook on linear regression and other foundations of predictive analytics
mb
48min
82min
Jupyter notebook demonstrating the use of Pandas to load and work with data, various concepts on explanatory data analysis, and standard data preprocessing operations
mb
mb
Introduction - Part 1
28min
Introduction - Part 2
29min
mb

Requirements

  • - no specific requirements - working knowledge of multivariate statistics is useful - prior experiences with computer programming are helpful but not mandatory

Description

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About the Instructor

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About the Instructor

Stefan received a PhD from the University of Hamburg in 2007, where he also completed his habilitation on decision analysis and support using ensemble forecasting models in 2012. He then joined the Humboldt-University of Berlin in 2014, where he heads the Chair of Information Systems at the School of Business and Economics. He serves as an associate editor for the International Journal of Business Analytics, Digital Finance, and the International Journal of Forecasting, and as department editor of Business and Information System Engineering (BISE). Stefan has secured substantial amounts of research funding and published several papers in leading international journals and conferences. His research concerns the support of managerial decision-making using quantitative empirical methods. He specializes in applications of (deep) machine learning techniques in the broad scope of marketing and risk analytics. Stefan actively participates in knowledge transfer and consulting projects with industry partners; from start-up companies to global players and not-for-profit organizations.