As many may be aware, our CEO, Dr. Kampakis, is the author of three influential books on AI and data science. Two of these were published with APress, a renowned branch of the global scientific publishing giant, Springer. The first one is called “The Decision Maker’s Handbook to Data Science”, and the second one being “Predicting the Unknown – The History and Future of Data Science and AI”.
Recently, Dr. Kampakis was invited to a Springer Nature event where he showcased the central ideas from two of his works published by them. He delved deep into subjects like foundational data science, the trajectory of AI’s evolution, and the anticipated future trends. Additionally, he recounted his journey as an author with Springer Nature, highlighting his collaboration with Apress and his experience with their AI-integrated processes.
During the discussion with the audience, we delved into a range of topics, including the following.
Organizations nowadays typically adopt a data strategy. Instead of collecting data themselves, Data Scientists are tasked with analyzing the data handed to them. There’s a growing trend of outsourcing to AI consultants, especially in specialized sectors such as finance where the quality of data is of utmost importance.
Librarians face challenges in data analytics, as many current tools they use lean towards being manual and basic. While modern tools like Power BI can bring about improvements, it’s the foundational understanding of data science principles that truly matters. Excel might not be the go-to for such tasks, but emerging AI tools like Chat GPT, particularly when paired with Python, can elevate the data analysis process.
Data quality stands as a crucial checkpoint for any organization. Regular checks for inconsistencies such as missing values or typographical errors are a must. Employing predictive models and interactive dashboards can be a game-changer, offering insights into performance metrics and flagging potential issues.
A noticeable communication gap exists between diverse domains, be it data science, blockchain, or library management. The industry feels the absence of resources that can bridge this divide, making the flow of ideas smoother and more coherent.
Pitching the significance of data science to the top tiers of an organization can be a challenge. It’s essential to back up claims with solid education and examples. A city like London, bursting with intellectuals and PhDs, still witnesses a gap in professionals who can seamlessly blend data science expertise with practical business knowledge.
Open data sharing is creating waves in the scientific community. Tools like blockchain promise a quicker pace of research, opening avenues to novel ideas. However, achieving a harmonious rhythm in centralized systems remains a challenge.
On the horizon of technological advancements, quantum coupling paired with AI analysis holds the potential to be a groundbreaking force. Lastly, for forward-thinking movements like DeSci to gain traction in the scientific world, lowering the entry barriers is vital, and AI might just be the key to unlock that door.