Sandeep Trivedi is a leading SAP Application Development, Principal at LyondellBasell Chemical, in Houston, Texas, United States. He is SAP certified and specializes in SAP Analytics Reporting using machine learning algorithms. He is not only an SAP Analytics Consultant, but also is considered a visionary, due to his significant contributions in research. He is a Senior Member of IEEE, U.S., Region 5, and the American SAP User Group (ASUG). Prestigious IEEE conferences, including ICCCIS-2022, IEEE UEMCON 2022, and IEEE ICSCA 2022, invite Sandeep Trivedi to serve as a technical reviewer because of his superior expertise in Artificial Intelligence, Machine Learning, and Deep Learning.
Q1. What are the key skills that you use every day as a data scientist, and how did you develop them?
Data scientists are in high demand as companies across industries look to leverage the power of data to gain a competitive edge. As a data scientist, the key skills I use every day include programming, statistics, data analysis, machine learning, and effective communication (a soft skill). I honed my programming skills through courses and real-world projects, while my statistical knowledge was gained through formal education and hands-on experience. I developed my data analysis and machine learning skills by working on complex datasets and using cutting-edge ML algorithms. Effective communication, which is critical in explaining complex concepts to non-technical stakeholders, is something I acquired through practice.
Q2. What are the top challenges you currently face as a professional data scientist, and how do you go about tackling them?
Top challenges in data science are data quality, model interpretability, and ethical considerations. Data quality is crucial for accurate analysis, but it’s often messy, incomplete, or biased. Model interpretability is important for understanding how a model works and why it makes certain predictions. Ethical considerations are becoming more important as we use data to make decisions that affect people’s lives.
Another challenge is staying up-to-date with the latest advances in machine learning and artificial intelligence, which I address by attending conferences, staying current on recent research papers, and working on personal projects to apply new techniques.
Q3. How is Machine Learning transforming the Digital Enterprise industry?
Machine learning is transforming the Digital Enterprise industry by enabling more efficient and reliable management of networks. It is being used to optimize network performance, to identify and address network issues, and to automate network configuration. Machine learning is also being applied in the areas of security and predictive maintenance, where it helps to detect and prevent network breaches and predict when network components will require maintenance.
Q4. How important is the domain knowledge of the business/industry you’re in as a data scientist, and how did you acquire it?
Domain knowledge is crucial for a data scientist as it allows them to understand the data and develop models that are tailored to the specific needs of the business or industry. I acquired my domain knowledge through collaboration with subject matter experts, attending industry events and conferences, and reading industry publications and reports.
Q5. 3 words that best summarize how you learned ML and data science?
Practice, persistence, and passion. These words highlight the importance of hands-on experience and continuous learning to develop and refine skills in machine learning and data science.
“Practice” emphasizes the value of actively working on projects and experiments to gain practical skills and knowledge. “Persistence” speaks to the need to stay committed and persevere in the face of challenges and setbacks. Finally, “Passion” reflects the importance of a strong interest and enthusiasm for the field, which can help drive motivation and engagement in the learning process.
Q6. What advice would you give to someone who wants to get into data science today?
My advice for aspiring data scientists is to start by learning the basics of programming, statistics, and data analysis. They should then work on real-world projects and personal projects to gain hands-on experience. Finally, they should network with other data scientists and seek out mentorship and guidance from experts in the field.
Q7. What inspires you about working in Data Science?
What inspires me about working in data science is the ability to use data to solve complex problems and make a positive impact on society. It is also exciting to be working in a rapidly evolving field that is constantly pushing the boundaries of what is possible.