Project Khulo: Career Path Tracking for Better Interventions

Project Information

  • Stakeholder: CSIR MDS, DST, NRF, CSIR
  • Students: Olwethu Madikwa (WSU), Louisa Sommerville (UP), Carel Chach (SMU)
  • Project Lead: Ms. Nyalleng Moorosi, Dr. Vukosi Marivate
  • Project Mentors: Ofentswe Lebogo
  • Year: 2016/2017

Project Description

In the 1st phase, the team developed a Web App that will help Institutions such as the CSIR find sub careers that have the potential of later becoming data science and further help address the shortage of data scientists. Khulo analysed how a person with a Computer Science or Electrical Engineering qualification can potentially become a Data Scientist. Khulo’s initial aim was to help a user find the quickest routes to becoming a Data Scientist, while on the other hand helping Institutions fund those sub careers with the potential of becoming Data Scientists. Project Khulo’s observations, on the data they retrieved from a career portal, was that ICT jobs are more in Johannesburg and Cape Town compared to Finance jobs that are widely available all over the country. They found that the job market for ICT jobs prefer people who are familiar with programming language Java, Python, C and PHP.

During the 2nd phase Project Khulo created two main branches in their Web app, the first branch focused on Career analysis which comprises of analysing two main sectors, namely the government sector and the ICT sector. Each sector was then further broken down to understand each job market in terms of jobs available, skills required, remuneration and locations of each job. The second branch focused on trajectory analysis which concentrates on individuals and their qualifications and skills available. An analysis of the top skills demanded by the job market is provided and the skills gap is then given a set of skills where the individual can improve on to make themselves more marketable within the current job market. Khulo’s aim was to understand how the job market evolves and to provide individuals with the tools to improve their skills and become more marketable. The output on each of these pages is career analysis graphs and sector analysis and career trajectory trending skills and skills required to bridge the gap.

Student Remarks


The students have learned to extract data, clean text and data, clustering, search, graphical analysis, machine learning and topic modelling.

Author: Team + Nolihle Gulwa, B Tech Journalism, Walter Sisulu University.