Data Science and Machine Learning for Managers
Business Analytics with R
- 6 ECTS module
- Master students in Technology Management (NIT)
- Students with a strong interest and motivation in acquiring the skills required for mastering the computational aspects of modern business analytics.
Data Science is the science of extracting knowledge and information from data and requires competencies in both statistical and computer-based data analysis. This module is part of our complementary studies which are supposed to familiarize students with the entrepreneurial challenges of the future and expand their knowledge on important aspects of technology management.
In this module, students learn how to acquire, cleanse, and transform large amounts of data online using various techniques. The aim is to explore, visualize, and model the related data in a target-oriented way, using modern methods of machine learning. This is a class for programming with R. It is designed for non-programmers to provide a friendly introduction to the R language, with hands-on examples. Throughout the class, you will use your newfound skills to solve practical data science problems.
Over the course of seven days, each with two sessions, students will create a coding portfolio demonstrating a variety of data-analysis and communication skills. Each session will involve a small amount of lecturing on R concepts, and a large amount of time for students to complete coding and analysis problems assigned on a daily basis.
After completing this module, 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 with respect to classifications and predictive predictions
- Communicate data and results in the form of products and applications
Time & Location
Course Notes & Materials
Access to course notes & materials here.
|1||June 12th||Introduction to R, RStudio IDE & GitHub|
|2||June 19th||Introduction to the tidyverse|
|3||June 26th||Data Acqusition|
|6||tbd||Fundamentals of Machine Learning|
|7||tbd||ML: Performance Measures|
|8||tbd||Supervised ML: Classifaction|
|9||tbd||Supervised ML: Regression|
|10||tbd||Unsupervised ML: Clustering|
|11||tbd||Unsupervised ML: Dimension Reduction|
|12||tbd||ML: Deep Learning|
|13||tbd||Reporting with RMarkdown|
|14||tbd||Reporting with Shiny|