Harnessing data for better healthcare via healthtech

Driven by a desire to see businesses thrive, Adeleye Olaniyan transitioned into data science. As a master’s student in Artificial Intelligence and Data Science, his keen interest in interpreting and analyzing data, identifying trends, and uncovering patterns has proven invaluable. In this interview, the data scientist shares his journey and insights.

How did your journey into AI and data science go?

My journey into AI and Data Science has been quite the adventure, I must say. You know, as a child, I was always curious about how things work. Growing up in Nigeria, I was that kid who loved to take things apart and see what was inside. This curiosity naturally led me to pursue a degree in computer science.

After my undergraduate studies, I started my career as an IT Analyst. It was a rewarding experience because it allowed me to understand the backbone of technology infrastructure. But you know how it is, now – in this tech world, one must keep evolving. I saw how the world was moving towards data-driven decisions and intelligent systems, and I knew I had to be part of that future.

So, I decided to dive deeper and became a Python developer. Python is such a versatile language, perfect for both scripting and more complex applications. This role honed my problem-solving skills and gave me the opportunity to work on various projects that required me to analyze data and build algorithms. It was during this period that my passion for AI and Data Science really blossomed.

Currently, I am pursuing my master’s in Artificial Intelligence And Data Science. This program has been an eye-opener and has given me a solid foundation in both theoretical and practical aspects of the field. It’s fascinating to see how data can be transformed into actionable insights and how AI can revolutionize industries.

In a nutshell, my journey has been about continuous learning and adapting. From understanding IT systems as an analyst to coding sophisticated algorithms as a Python developer, and now, delving deep into AI and data science, it’s been a fulfilling path. I am excited about what the future holds and how I can contribute to the growth of AI and Data Science, not just globally, but right here in Nigeria.

How has it been so far pivoting into data science with your background in engineering?

Ah, it has been quite an enriching and transformative experience! My background in engineering provided a solid foundation in problem-solving and analytical thinking, which are crucial skills in data science. Transitioning into this field, I found that many of the principles I learned in engineering, such as systematic analysis, attention to detail, and the ability to approach problems methodically, were directly applicable.

However, the journey wasn’t without its challenges. Data science is a vast and ever-evolving field, so there was a steep learning curve initially. I had to familiarize myself with new concepts and technologies, such as machine learning algorithms, statistical analysis, and big data tools. Thankfully, my prior experience as an IT analyst and Python developer made this transition smoother. I was already comfortable with data manipulation, programming, and algorithm development, which are essential aspects of data science.

In my role as an IT analyst, I developed a strong foundation for understanding business processes and identifying key areas where technology could drive improvement. This experience has been invaluable in my data science career, as it allows me to approach data problems with a business-oriented mindset. As a Python developer, I honed my coding skills and gained experience in building and deploying software solutions. These skills have been directly applicable to my work in data science, where I often need to write code to clean, analyze, and visualize data.

One pivotal experience in my transition was working with a healthtech startup where I served as a Data Scientist and Web Developer, where I was responsible for developing predictive models and creating a web application to predict diseases. This role allowed me to apply my technical skills to a real-world problem, significantly impacting patient outcomes and healthcare delivery. Collaborating with healthcare professionals and software engineers, I ensured our solutions were both technically robust and practically useful. This hands-on experience was invaluable, reinforcing the importance of data science in driving innovation and improving lives.

Currently, as a master’s student in Artificial Intelligence and Data Science, I have been able to deepen my knowledge and gain new insights into advanced techniques and tools. The academic environment has been very supportive, providing both theoretical knowledge and practical applications. The coursework and projects have allowed me to explore various facets of AI and data science, from deep learning to natural language processing. Additionally, collaborating with fellow students and professors has been incredibly enriching, as it has exposed me to diverse perspectives and ideas.

One of the most rewarding aspects of this journey has been seeing the tangible impact of data science on business outcomes. Whether it’s through optimizing processes, predicting trends, or uncovering hidden patterns, data science has the power to drive significant improvements and innovations. This aligns perfectly with my desire to see businesses thrive and succeed.

Overall, it’s been a fulfilling and exciting journey. I’m continuously learning and growing in this dynamic field, and I’m eager to see what the future holds. The combination of my engineering background, industry experience, and advanced studies has equipped me with a unique skill set that I believe will enable me to make meaningful contributions to the world of data science and beyond.

Can you describe the healthtech startup you worked with and your role there?

I had the opportunity to work with a dynamic healthtech startup in Lagos. The startup is focused on leveraging advanced technologies to enhance early disease detection and improve patient outcomes. The startup’s primary goal is to develop innovative solutions that assist healthcare professionals in making more accurate and timely diagnoses.

In my role as a Data Scientist and Web Developer, I was responsible for developing predictive models and creating a user-friendly web application aimed at disease prediction. My responsibilities included gathering and preprocessing data, selecting appropriate machine learning algorithms, and building models to predict the likelihood of various diseases based on patient data.

Additionally, I collaborated with a multidisciplinary team of healthcare professionals, software engineers, and data analysts to ensure that our solutions were both technically sound and practically useful in real-world medical settings. I also focused on ensuring that the web application was intuitive and accessible for both healthcare providers and patients, incorporating feedback from users to continuously improve the platform.

Overall, my role was multifaceted, involving both technical development and cross-functional collaboration to drive the success of our disease prediction initiatives.

How are Nigerian businesses faring in terms of data usage?

While credit is due to the NBS for their efforts in using data to evaluate and monitor economic indicators, the same cannot be said for most businesses in the country. It is quite rare for businesses to collect and analyze customer data to determine how best to satisfy customers and improve operations.

Recently, we have seen well-established businesses introduce price changes without consulting their customers to understand their preferences or the types of changes they would be interested in regarding service delivery. In an open market like Nigeria, where substitutes are readily available, businesses need to put more effort into occasionally analyzing customer preferences.

How do you plan to contribute to the growth and development of data science in Nigeria?

As a data science master’s student with a deep understanding of the unique challenges and opportunities within Nigeria, I am committed to contributing to the growth and development of data science in several key ways.

Firstly, I plan to leverage my technical skills and knowledge to drive impactful projects that address pressing issues in various sectors such as healthcare, finance, agriculture, and education. By developing data-driven solutions, I aim to help organizations make more informed decisions, optimize their operations, and ultimately improve the quality of life for Nigerians.

Secondly, I am passionate about education and knowledge sharing. I intend to collaborate with academic institutions, professional organizations, and online platforms to develop and deliver training programs and workshops. These initiatives will focus on building the capacity of aspiring data scientists and enhancing the skills of current practitioners. By fostering a strong community of data science professionals, we can collectively tackle complex problems and drive innovation.

Additionally, I plan to advocate for the ethical use of data and the establishment of robust data governance frameworks. Ensuring that data is collected, stored, and analyzed responsibly is crucial for building trust and maximizing the benefits of data science. I will work with policymakers, industry leaders, and other stakeholders to promote best practices and develop policies that support the ethical and effective use of data.

Lastly, I will actively engage in research and collaboration with both local and international experts. By contributing to academic research and participating in cross-disciplinary projects, I aim to bring global best practices to Nigeria and adapt them to our local context. This approach will help in advancing the field of data science and ensuring that Nigeria remains competitive in the global data economy.

Through these efforts, I am confident that we can create a vibrant and sustainable data science ecosystem in Nigeria that drives economic growth, social development, and technological innovation.

How do you ensure the ethical use of data in your work?

Ensuring the ethical use of data is a paramount consideration in all my data science endeavours. My approach involves several key principles and practices.

Firstly, I adhere to strict data privacy and protection standards. This means ensuring that any data I work with is anonymized and encrypted to protect individuals’ identities and sensitive information. I am committed to following relevant data protection regulations, such as Nigeria’s Data Protection Regulation (NDPR) and the General Data Protection Regulation (GDPR) for any international data.

Secondly, I prioritize transparency and accountability. I believe it is essential to maintain clear documentation of data sources, methodologies, and any transformations applied to the data. This transparency allows for reproducibility and ensures that others can verify and trust the results of my analyses.

Thirdly, I advocate for and practice informed consent. Whenever possible, I ensure that individuals whose data is being collected are fully aware of how their data will be used and have given explicit consent. This respects individuals’ autonomy and right to control their personal information.

Furthermore, I strive to minimize biases in data collection and analysis. I am aware that biases can arise at various stages, from data sampling to algorithm design. To mitigate this, I rigorously test and validate my models, seek diverse data sources, and remain vigilant about potential biases that could influence outcomes.

I also prioritize the principle of “do no harm.” This means carefully considering the potential impact of my work on individuals and communities. I assess whether the insights derived from data analysis could lead to unintended negative consequences and take steps to mitigate such risks.

Lastly, I engage in continuous education and collaboration with peers and experts in the field. Ethical considerations in data science are constantly evolving, and staying updated with the latest best practices and ethical guidelines is crucial. I participate in workshops, seminars, and professional forums to keep abreast of new developments and to contribute to the broader discussion on data ethics.

By integrating these practices into my workflow, I aim to ensure that my work in data science not only advances knowledge and innovation but also upholds the highest standards of ethical responsibility.

What data-driven approaches can be developed to optimize the deployment of renewable energy infrastructure in Nigeria?

Optimizing the deployment of renewable energy infrastructure in Nigeria is crucial for sustainable development and energy security. Data-driven approaches can play a significant role in achieving this.
Geospatial analysis can be used to identify optimal locations for renewable energy projects. By analyzing geographical data, we can determine the best sites for solar, wind, and hydroelectric power plants. For instance, solar irradiance data can help identify regions with the highest potential for solar energy, while wind speed data can pinpoint areas suitable for wind turbines. This ensures that investments are made in locations with the highest energy generation potential.

Creating detailed maps of renewable energy resources is essential. Using data from satellites and ground-based sensors, we can map solar radiation, wind patterns, and water flow rates across Nigeria. These maps can guide policymakers and investors in identifying the most promising areas for renewable energy development.
Accurate forecasting models can be developed using historical energy consumption data, population growth trends, and economic indicators. By predicting future energy demand, we can plan the deployment of renewable energy infrastructure to meet anticipated needs. This helps in balancing supply and demand, reducing the risk of energy shortages or overproduction.
Analyzing the existing power grid’s capacity and identifying areas that need upgrades or expansions is critical. Data on grid load, transmission losses, and infrastructure conditions can help in planning where new renewable energy sources can be integrated without overloading the grid. This ensures a stable and reliable energy supply.

Economic data and financial models can be used to assess the cost-effectiveness of different renewable energy projects. By comparing initial investment costs, maintenance expenses, and expected energy output, we can prioritize projects that offer the best return on investment. This approach helps in making informed decisions about where to allocate resources.
Using data on local ecosystems, wildlife, and land use, we can evaluate the potential environmental impacts of renewable energy projects. This helps in selecting sites that minimize ecological disruption and comply with environmental regulations. Geospatial data can also be used to monitor the environmental impact over time.

In summary, data-driven approaches are key to optimizing the deployment of renewable energy infrastructure in Nigeria. By leveraging geospatial analysis, resource mapping, demand forecasting, and real-time monitoring, we can make informed decisions that maximize the benefits of renewable energy while minimizing costs and risks. This holistic approach ensures that Nigeria can achieve its renewable energy goals efficiently and sustainably.