Adeola Balogun, a young female 400-level Computer Science undergraduate from the Obafemi Awolowo University, Ile Ife, has won the Data Science Nigeria (DSN) bootcamp hackathon.
The bootcamp included very intensive hands-on classes, group learning sessions, and three individual hackathons, featuring a hands-on focus on machine learning algorithms, engineering statistics, and the broad principles of data science applications in solving real word problems.
The learning sessions were spiced up with three ongoing hackathons aimed to stimulate the 150 participants, who qualified for the bootcamp through pre-qualification exams on Microsoft/Edx Data Science MOOC series and DataCamp followed by a public Kaggle competition.
In his welcome address, the bootcamp convener, Mr. Bayo Adekanmbi, MTN’s Executive, said “We must focus on world-class capacity building in a consistent and rigorous manner if we truly want to build high-impact analytics products that can accelerate our national development and position Nigeria to become the data science hub for the continent, with the potential to access as much as 10% of the global data analytics outsourcing market, which is expected to garner $5.9 billion by 2020”.
At the award ceremony, Adeola said, “I only started taking data science three weeks ago, but I have carefully followed the instructions and guidance provided by the instructor to become the first on the competition leader board.”
Adeola Balogun began programming in Python during her 200-level classes. She is passionate about encouraging ladies interested in programming in Python, and she complements her academic studies by working as an intern with the Elo Umeh-led Terragon Group.
She is also an advocate for the Women Techmakers movement, and a Google Ambassador on her campus. The other top four participants in the Kaggle competition are Chibueze Oguejiofor, Onimisi Esho, Sarah Adeola, and Aminu Onimisi Abdulsalami.
According to the participants, the bootcamp was an eye-opening, skill sharpening, and rigorous experience that provided a full immersion in the realities of the field. Data Science has been rated the number one career in the USA today, according to Glassdoor’s Best Jobs in America list for both 2016 and 2017.
The bootcamp participants also had chance to learn from leading industry experts, who shared real world information about the intersection of data science and machine learning for solving business problems. Mr Ladi Aduni, Associate Director, Technology Advisory KPMG led the briefing on the KPMG-sponsored Segmentation Hackathon at the bootcamp.
The hackathon required participants to use unsupervised learning to group banking customers based on many unrelated variables. The top-rated participants in this hackathon will qualify for internships and possible job placements at KPMG Nigeria.
Mr. Ngozi Dozie of OneFi discussed how to build industry-ready algorithms that can solve real-world problems, using the example of how weather forecasting correlates with loan defaults.
He said, “When the weather is bad, people default more on their loans, and data science must be able to explore other non-obvious variables in making business prediction solutions”.
Mr Usoro Usoro, General Manager of Digital Financial Services at MTN Nigeria engaged the participants on how to leverage the robust intelligence of big data algorithms to unravel local opportunities in the payments and lending fields.
Professor Raj Krishnan, a Microsoft Azure expert and adjunct professor at the Illinois Institute of Technology in Chicago, led a hands-on immersion in machine learning using the cloud-based Azure platform. Dr Johnson Iyilade, the Founder and CEO of Glomacs IT Solutions and Services contributed from Canada, exploring how we have moved from a system-centric to user-centric connected data architecture, featuring practical explorations of the major big data technologies (Hadoop, HDFS, MapReduce, etc.).
Wale Akinfaderin, a doctoral researcher and expert data scientist, dialled in from the USA to discuss feature engineering, data preparation, over fitting, and managing missing data (including mean, median, mode, regressed value and even nearest neighbour values).
