Event Details

Data Science Industrial Training for CIVE Students

Event Date:Jul 22, 2019

ParrotAI in collaboration with the College of Informatics and Virtue Education hosted industrial Practical training in machine learning and data science for CIVE students. The training was to equip students with knowledge of data science, machine learning, and problem-solving skills.
The training was designed to help students grasp both theoretical and practical foundations of data science and machine learning.

We designed training packages based on the experience of our target group. For example, we developed a comprehensive program (lectures and notebooks) for first-year students. As for continuing students, we provided them relevant MOOC and other online courses to understand specific concepts and use these online materials during the training. The online approach was used in the latter group because for continuous students the program is based on guided self-learning in order to enhance their independent learning skills.


To assess the knowledge acquired during the training, students were assigned tasks so as to apply skills gained during the training. These assignments were part of the daily activities. In the final assessment, we involved group projects on data science and machine learning. We provided students with open data sets from http://opendata.go.tz/, https://www.kaggle.com/, https://zindi.africa/ and https://competitions.codalab.org/competitions/20100#learn_the_details in order to accomplish their specific projects. Among the work conducted by students include

  • Health facility problem in Tanzania.
  • Urban water services provision from district and township water authorities.
  • Financial Inclusion in Africa.
  • Water kiosks distribution in Tanzania.
  • Water Supply And Sanitation Authority.
  • Unemployment dataset analysis.
  • Building segmentation from satellite or drone images.

From the student’s feedback regarding the short time allocated for the training, we urge universities to review their programs and include machine learning and data science courses at different levels. The introduction of such courses should focus on strong theoretical foundations and practicals so as to ensure that students become experts in the field by the end of the degree program. We also recommend other universities to invest in building skills of their first year’s students in Computer Science by offering them comprehensive training in particular technologies during fieldwork instead of attaching them to industries. This is because most of the courses taught in the first year are introductory courses, and therefore, enhancing their skills through intensive training is more relevant than field placement in order to build their competence in skills.





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