Please visit also the course website for current information: https://www.startupengineer.io/_courses/dat_sci/

The course "Business Decisions with Machine Learning" is part of an applied and interactive course program on Business Analytics, which is designed to provide you with a sound understanding of the constantly growing opportunities that business analytics experiences through modern approaches in data science and machine learning. In this course you will learn methods of descriptive, predictive and prescriptive analytics in order to approach critical business decisions based on data and to derive recommendations for action. Participants learn how to collect, cleanse and transform large amounts of data using various techniques. The aim is to specifically examine, visualize and model the associated data using modern machine learning methods.

During the course program, the participants apply the tools they have learned to practical data science problems from various management areas, creating a comprehensive and multifaceted application portfolio that demonstrates their data analysis and modeling skills. The programming language used is R, whereby the integration of Python into the workflow is also practiced. Programming knowledge is not required, but is of course an advantage. Each session will involve a small amount of lecturing on R concepts, and a large amount of time for students to complete assigned coding and analysis problems.

Topics covered in this course are:
1. Fundamentals of Machine Learning
2. Supervised ML: Regression (I)
3. Supervised ML: Regression (II)
4. Automated ML with H20 (I)
5. Automated ML with H20 (II)
6. ML Performance Measures


After completing this course, students will be able to:
- Obtain large amounts of data via APIs or web scraping from the Internet
- Clean and transform data
- Explore and visualize data in a goal-oriented way
- Model data using modern machine learning techniques


Grading
Students are evaluated based on their solutions of challenges assigned in each session, which they continuously document in their github lab journals.