Data Science & Machine Learning for Managers

Elective Module by NIT

Prof. Christopher Ihl

Credit Points: 6

Semester: 1

Lecturer: Prof. Christoph Ihl

Examination Form: Written elaboration and project work

Learning Outcomes:

Upon completion of this module, the students would be able to:

  • Acquire large amounts of data from the Internet via APIS or through web scraping

  • Cleanse and transform data

  • Explore and visualize data in a goal-oriented manner

  • Model data using advanced machine learning techniques with respect to classifications and predictive forecasts

  • Communicate data and results in the form of products and applications

Content:

  1. Basic Workflows in Data Science

  2. Programming Basics: Functions, Loops, Application

  3. Data Access, Scraping and Import

  4. Data Transformation with dplyr and data.table

  5. Dealing with Text Data

  6. Exploratory Data Analysis

  7. Data Visualization with ggplot2

  8. Data Modelling Overview

  9. Unsupervised Machine Learning

  10. Supervised Machine Learning

  11. Deep Learning

  12. Data Communication: R Markdown, Shiny Dashboards