The Perspective of Multi-stakeholder on Data Science

30th August 2023 | News

On 30th August, 2023, there was an event titled “The Perspective of Multi-stakeholder on Data Science“. This assembly of minds, ranging from those in the banking sector to educators and researchers.

    The event was divided into 4 sections focusing on a different aspect of speakers: 

    Data Science in the Banking sector

    Mr. Amit Anand, Director of Data Analytics at Wing Bank, presented his company’s profile, the challenges and solutions in the banking sector, and the role of data analytics and machine learning in achieving business goals. He shared his 18-year experience in banking and his vision to create data science jobs in Cambodia. He also explained how data analytics can help predict revenue and identify churn customers.

      The Important of Data in AI

      Dr. Srun Sovila, Director of NICC, RUPP, gave a speech on AI, IoT, and Data Mining. He showed a video of the AI revolution and how AI systems can mimic human intelligence. Moreover, He explained the steps to create an AI system from data selection, cleaning, transformation, pattern finding, and evaluation. He also discussed the role of data engineering and IoT in collecting and processing big data. 

        The Learning Path in Data Science

        Mr. Kan Bonpagna provided invaluable guidance on kickstarting a journey in data science. Whether you’re considering online courses, intensive bootcamps, or academic degrees, his talk had something for every aspirant. In a complementary presentation, his detailed breakdown of the fields within data science and the expansive job opportunities was particularly beneficial for newcomers and seasoned professionals alike. 

          Data Science in Industry 4.0

          Dr. Kak Soky from IDT (CADT) bridged the past and future, Industry 3.0 and Industry 4.0. His talk served as a revelation of how IoT and AI are revolutionizing industries with smart factories and seamless machine communication. Diving deeper, he elaborated on the pivotal role of data, the intricate stages of the data science life cycle, and the potential challenges we may face as we march into Industry 4.0. 

            One of the standout moments of the event was an engaging panel discussion addressing a question on:  

            “Collaboration and Sharing: How can different stakeholders collaborate and share data for the common good, while maintaining trust and transparency?” 

            The speakers unanimously voiced concerns over data privacy. In the world of data-sharing, striking a balance between collaboration and confidentiality is taken into consideration. It was encouraging to see all stakeholders agree that open dialogue and discussions are crucial in reaching a consensus on data sharing practices. Transparency and trust, they stressed, are the pillars on which any collaborative effort in data science must stand. 

            These are the key takeaways from the discussion: 

            1. Open Dialogue: Before any data-sharing initiative, all parties involved should engage in thorough discussions to set boundaries and establish trust.
            2. Privacy First: Collaborative efforts should always prioritize individual privacy. Data anonymization and aggregation can ensure that while datasets are shared, individual identities remain protected.
            3. Standardized Protocols: Establishing industry-wide standards for data sharing can remove ambiguity and foster a more transparent environment.
            4. Ethical Responsibility: Data is not just numbers; it represents people. All stakeholders should approach data sharing with an ethical responsibility to ensure the well-being and rights of individuals are always prioritized.

              The event seamlessly to questions from participants: How to gear up for internships and anticipate future job needs in Data Science and AI. 

              The advice from speakers for students was to cultivate a robust portfolio. This is not just about showcasing technical expertise but also about demonstrating real-world problem-solving abilities, adaptability, and a continual learning mindset: The students and job seekers should focus on: 

              1. Hands-on Projects: Engage in projects that allow you to apply theoretical knowledge. This shows potential employers your practical skills.
              2. Networking: Building relationships in the industry can lead to internship opportunities and provide invaluable insights.
              3. Stay Updated: The world of AI and Data Science is rapidly evolving. Regularly update your skills and knowledge to stay ahead of the curve.
              4. Soft Skills: While technical proficiency is vital, soft skills like communication, teamwork, and critical thinking are equally important.
              5. Tailored Resumes: Customize your resume for each application, highlighting relevant skills and experiences.
              6. Continuous Learning: Consider additional certifications, workshops, and courses that can provide an edge in this competitive field.

              In closing, this event was not just about understanding the current landscape but also about preparing for the challenges and opportunities that lie ahead. For those passionate about data science and AI, the future is indeed promising, but preparation is key. 

              Join us as we continue to explore, learn, and shape the data-driven future!  


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