Kaduna Data Science Fellowship Programme 2023/ How to Apply

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The Click-on Kaduna Data Science Fellowship Programme 2023 application portal will be open on the 1st of July 2023. Interested Applicants should visit the application link below to apply.

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The Click-On Kaduna Data Science Fellowship Programme is a Digital Skills and Development Programme implemented by the Kaduna Bureau of Statistics which is aimed at training data management professionals. A total of 100 young people will be trained in Data Science and the use of modern tools to improve public policy and decision-making in Kaduna State.

The Data Science Fellowship Programme is part of the Kaduna State Data Lab Project jointly implemented by the Kaduna State Government, Bill and Melinda Gates Foundation and Natview Foundation for Technology Innovation

Program Duration

The Programme is expected to last for a 6-month period divided into a 3-month intensive training programme and a 3-month paid internship, where participants would have an opportunity to work closely with the Amina J. Mohammed SDGs Data Lab under the Kaduna State Bureau of Statistics.

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Fellows will also be posted to MDAs especially the State Ministry of Health and Other Agencies to support in data coordinating efforts that will provide impact to society.

SEE ALSO: APPLY: 2023 AI/ML/Data Science Training Scholarship For Young Nigerians

Program Benefits

Participants of the programme will benefit from the following:

a) A chance to upgrade your skills and develop a career in the most sought-after industry in the world.

b) An opportunity to work with a team of public policy experts, Data Analysts to contribute to development planning, public policy, and the use of data for evidence-based decision-making in the Health Sector.

c) 6-month fully paid internship programme with industry leaders and stakeholders within the health sector.

d) Model data using Artificial Intelligence and Machine Learning tools within the Amina J. Mohammed SDGs Data Lab.

e) Build a sustainable network within the Data Science Ecosystem.


1) Age: 18yrs — 35yrs except for exceptional cases

2) Location: The State of residence must be in Kaduna

3) Academic Qualifications: Graduate of Tertiary Institutions in any of the STEMS courses particularly: Statistics, Mathematics, Computer Science, Engineering, Economics, Physical Sciences, and any other health-related discipline. Undergraduates within the STEM fields identified who are doing their 6-month to 1-year Internships are also encouraged to apply.

4) Competency Level: We are looking for people with the following skills:

  • Basic Computer Knowledge
  • Basic Knowledge around using Data & Understanding of Microsoft Excel
  • Problem-Solving Skills
  • Communication Skills(Written and Verbal English)

5) Gender: 50% of gender balancing and distribution will be considered during the selection process.

Deadline:not specified

SEE ALSO: UNP Data Science Internship Program: Mastering Data Science & Machine Learning with Python (July-Sept 2023)

How to apply

Step 1: Interested Applicants should visit this link https://www.datascience.click-onkaduna.ng/apply-now to Fill and Submit the application form.

Step 2: You will receive an email with an activation link

Step 3: Click the activation link to activate your profile

Step 4: Create a new password then Login to your profile

Step 5: Write an essay of not more than 250 words. Why you should be selected for the programme, and Upload a valid means of identification (i.e Voters card, National ID Card, Driver’s License or International Passport) NB: Image type should be .jpg

Step 6: Upload a video of not more than 2 minutes telling us what makes you stand out

Step 7: You will get a confirmation status of your application

Step 8: If successful, then you will be contacted to take a Computer Based Test

Step 9: If successful, then you will be contacted to attend a One on One Interview with the Selection Panel.

Step 10: Join the Second Cohort for the Fellowship Programme

For more information, visit the Click on Kaduna program portal

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