Data Analytics with Python

Starting with lectures on innovation management theory, the students were introduced to basic data analytical methods using Python, and with the help and supervision of external data scientists were able to carry out group projects of analysing data from a large idea-database, and later present results to their peers.


Digital competencies will be an important skill for students of innovation management in relation to the start and development of their careers. Many innovation-related jobs will require students to understand main computational techniques and how to use them in order to make sense of and evaluate volumes of data. These jobs also require the ability of students to effectively communicate and collaborate with people from many disciplines and with competences and skills in digital methods, such as data scientists. 

Use of technology

  • Python  

The students were introduced to Python programming in order to make sense of and analyse larger amounts of data. The software was suggested by Center for Humanities Computing as the most appropriate for this type of activity, as it was also the preferred tool for the data scientist.


The students of the course were interested in the topic, and their assignments and presentations were generally good. Some groups made some very sophisticated analyses, showing good mastery of the techniques they had learned and the ability to adapt them to their specific dataset.   

The students were excited at the possibility to work with data scientists.


The activity got the students acquainted with data analysis techniques for analysing large amounts of qualitative data (from an idea platform). To analyse the data, they had to collaborate with a group of data scientists. The students did not know how to program, but they knew what analysis criteria were relevant in relation to the strategy of the firm and in relation to the challenges of analysing ideas. From the collaboration with the data scientists, they learned not only relevant computational methods but also about how to collaborate and manage knowledge across disciplines. The following describes the process of the project:  

Before the first lecture: 

  • First, the students were introduced to the innovation management theory on Idea Management Systems to get an overview of the research in the area and to gain insights into the benefits and challenges of using idea management platforms.  

  • Secondly, the students were introduced to the most common techniques of analysing data. They had to read two articles: one on data analysis techniques and one on a study of entrepreneurship that used these techniques.

At the lecture: 

  • Then, the students attended a lecture by a researcher in humanities computing elaborating on the different techniques.  

  • The students worked in groups, looking at the dataset and deciding which types of technique and analyses they wanted to employ with the data.  

  • Furthermore, the students sent the requirements by email to the data scientists, describing what types of analyses they wanted to do and why.  

Supervision session: 

  • A supervision session took place, where each group met with the assigned data scientist and discussed the requirements they sent.  

  • Based on the feedback from the data scientists the students readjusted their first requirements and send an updated version to the data scientist.  

After the supervision session: 

  • After the supervision session, the data scientists prepared the programs needed for the students to run the required analyses and sent the programs by email with main results.  

  • Afterwards, the students ran the analyses and were asked to reflect on the obtained results.  


  • The students presented their project, explaining which criteria and techniques they had defined for analysing the data, and explained/reflected on the results obtained.  

  • The students also reflected on the process of collaborating with data scientists and what they had learned from this. 

Ressources and support

  • The students were given texts that were relevant to the course, slides, access to the Python code packages, and supervision from data scientists.  

  • The students got instructions, both oral and written, supervision from data scientists, and lastly, feedback from the instructors. 

Challenges and advice

  • Access to the necessary resources was a challenge, as the course draws on the expertise of data scientists who can help with the coding and the supervision every year. This is something that we need to keep in mind for the continuation and sustainability of the course. We rely on either the help of data scientist or the ability of the teachers to code at this level.  

  • Another challenge was the fact that the database was a bit too small to make interesting analyses. A bigger and more detailed dataset will often be preferable.

  • More iterations back and forth between the students and the data scientists would probably have increased the learning outcome, but it would also be more time consuming and therefore at the expense of other topics and activities in the course.

  • For students that are not very technologically savvy it is a good idea to focus on activities that reflect something they may end up using in their future career. This is important to spark their motivation for the task. In an attempt to accommodate this, a specific description of the skills they developed based in the course were given to the students, as a suggestion to what they could add to their professional resumes after participating in the activity. 

Pernille Smith on the teaching case:

Basic information:

Teachers: Pernille Smith and Michela Beretta

Faculty: BSS, Aarhus University

Discipline: Department of Management

Course: Managing Innovation

Level of study: MSc

Teaching method: Lecture

Number of students: 44

Duration: Short series of activities or lessons

Academic objective

The academic objective is for students to learn basic principles of data analytics, working on the database of a company's ideation platform. Using computational methods, students are introduced to how to analyse large amounts of qualitative data efficiently and they learn how to collaborate in interdisciplinary teams with data scientists.