GirlTechies Humanitarian Initiative, also known as Tech Girls Club, has trained secondary school girls in artificial intelligence (AI), data science and programming as part of efforts to increase female participation in technology and prepare participants for emerging opportunities in the digital economy.
   
The five-day AI/Data Science for Her bootcamp, sponsored by the Neural Information Processing Systems (NeurIPS), was held at Ahmadu Bello University (ABU), Zaria, for participants from the North-West zone.
  
The programme combined classroom instruction, practical exercises, group activities and project development, taking participants from basic computer literacy to Python programming, data analysis, AI and Machine Learning.
  
At the opening ceremony, Programme Lead Facilitator, Dr Wilson Sakpere, outlined the objectives of the programme and the skills and opportunities participants would encounter during the training.
  
The Deputy Dean, Faculty of Engineering, ABU, Prof. Laminu S. Kuburi, commended the initiative and encouraged the participants to maximise the opportunity to develop their skills.
  
Kuburi also stressed the importance of increasing the participation and representation of women in engineering and technology.
 
The Head of Department of Computer Engineering, Dr Basira Yahaya, similarly urged the young women to remain focused and persistent in pursuing their educational and career goals.
  
Yahaya, who drew from her experience as a female engineer, told the participants that women could make significant contributions to engineering and technology.
  
Other invited professionals and guests, including Dr David Obada and Dr Risikat Adebiyi, also encouraged and motivated the participants.
  
They emphasised the importance of continuous learning, confidence, determination, and taking advantage of opportunities for skills development.
  
The first phase of the training introduced participants to computer fundamentals and digital literacy, with emphasis on the safe, responsible and productive use of technology.
   
They also learnt about computer hardware and software, file and folder management, Internet safety, protection of personal information and the implications of their digital footprints. 
  
The participants were subsequently introduced to data and its applications in education, healthcare, agriculture, business and government planning before learning the basics of Microsoft Excel.
  
The Excel sessions progressed from data entry and formatting to sorting, filtering, formulas and data visualisation, with practical exercises involving school attendance, personal budgeting and survey responses. 
  
From spreadsheets, the training moved into programming, with particular emphasis on Python because of its applications in Artificial Intelligence, Data Science, robotics, cybersecurity and software development.
  
Participants learnt basic Python concepts, including variables, data types, input and output, before progressing to operators, conditional statements and loops.
  
They also developed simple programmes, including a calculator, student grading system and guessing game, to demonstrate how coding could be used to automate tasks and solve practical problems.
  
The training further exposed the participants to Pandas, a Python library for handling structured data, and demonstrated how programming could be used to organise, examine and analyse datasets.
  
A major component of the programme was the introduction to Artificial Intelligence and Machine Learning.
  
Participants learnt how machines use data to identify patterns, classify information and make predictions, while practical sessions demonstrated the relationship between training data, features and labels.
  
Using Google Teachable Machine, the participants created simple image-classification models, learning the basic process of collecting data, training a model, testing its predictions and improving its performance.
 
The training also addressed concerns around the responsible use of AI, including fairness, privacy, bias and accountability.
 
Participants were encouraged to regard AI as an assistant rather than an infallible source of information, and to verify important information before relying on AI-generated outputs.
  
The challenges considered included poor waste disposal, flooding, school attendance, water shortages, traffic congestion, crop diseases, plastic pollution and health awareness.
  
The final day featured capstone projects in which teams developed and presented solutions to selected real-world problems.
  
The projects could take the form of Excel dashboards, Python programmes, machine-learning models, surveys, presentations, posters or mobile application mock-ups.
   
During the project showcase, judges assessed the teams based on problem identification, creativity and innovation, application of AI or Data Science, technical implementation, teamwork and presentation skills.