Modern Workflows in Data Science

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Modern Workflows in Data Science - Course Details

Delve into the course contents and find out about the faculty members.

Single Course Price:

800.00 EUR (tax exempt)


Alexandru Cernat, PhD (University of Manchester)

Video lecture:

Alexandru Cernat, PhD (University of Manchester)


Course Dates:

To see all courses in the upcoming term click here.


This course is part of the Mannheim Data Science Certificate: R-Programming.

Book this course or the entire certificate here!

In order to book the course with alumni conditions, please get in touch with Manon Pfeifer directly.


Course Description

Short Course Description
Large data, fast pace of production, and collaboration are hallmarks of the new data environment. In this context, researchers must have a good understanding of data workflows and they must ensure consistent and reproducible practices in order to collaborate and consistently produce insights. This course deals with some of these essential topics. We will discuss the main types of workflows in data and survey sciences and how tools such as GitHub can enhance collaboration and insure reproducibility. We will also discuss the use of reproducible documents such as Rmarkdown or Jupyter Notebooks before covering the best practices for working with online data sources. We will finish the course by discussing the use of dashboards and how to develop such a tool using R Shiny.

Intro to Real World Data Management with R course or a good knowledge of R base and tidyverse.

Course Objectives
By the end of the course, students will…

  • understand the main types of workflows in data and survey sciences
  • understand the principles of reproducible workflows
  • know how to use Github to support reproducible flows
  • Understand the basics of reproducible documents
  • learn how to use Rmarkdown and Jupyter Notebooks
  • learn about the main types of storage for online data (e.g., SQL, JSON)
  • learn how to access distributed clusters using Spark
  • learn how to manage computing clusters
  • learn the principles of building a dashboard
  • learn how to build a dashboard using R Shiny

Course Composition
This is a 4 ECTS course that runs for 8 weeks. The content of the course is broken down into 4 topics, each topic will be covered in two weeks:

  1. Data workflows with Github
  2. Reproducible documents with Rmarkdown and Jupyter Notebooks
  3. Accessing data online
  4. Interactive dashboards with shiny

Learning and Teaching Methods
In this course, you are responsible for watching video-recorded lectures and reading the required literature for each unit prior to participating in mandatory weekly one-hour online meetings where students have the chance to discuss the materials from a unit with the instructor. In addition, students are encouraged to post questions about the materials covered in the videos and readings of the week in the forum before the meetings. Just like in an on-site course, assignments will be assigned and graded.

Grading will be based on:

  • Four homework assignments (worth 60% total)
  • Participation in discussion during the weekly online meetings and submission of questions via e-mail (deadline: Monday, 8AM before class) demonstrating understanding of the required readings and video lectures (worth 10%)
  • A final project (worth 30%)



ZFU Certification and Online Dispute Resolution

ZFU Certification

The Mannheim Master of Applied Data Science & Measurement program is certified according to the regulations of the ZFU (Staatliche Zentralstelle für Fernunterricht).


Online Dispute Resolution

Online dispute resolution according to Art. 14 Sect. 1 ODR-VO: The European Commission provides a platform for online dispute resolution (ODR). You can find more information under


Located in the heart of the German and European economy, Mannheim Business School (MBS), the umbrella organization for management education at the University of Mannheim, is considered to be one of the leading institutions of its kind in Germany and is continuously ranked as Germany’s #1.