This course was offered by IBM on Coursera, taught by Rav Ahuja, Alex Aklson, and Joseph Santarcangelo, and I’ve achieved a 95% grade. This introductory course provides a comprehensive overview of data science, its significance in today’s data-driven world, and the roles and skills of data scientists. It’s designed for beginners, requiring no prior knowledge, and serves as the first step in IBM’s Data Science Professional Certificate.
The course covers key topics such as the definition and evolution of data science, the roles and daily activities of data scientists, and the processes they use to analyze data. It includes insights from industry professionals, real-world applications of data science, and an introduction to tools like Python and Jupyter Notebooks. A peer-reviewed final project involves analyzing a data science job posting to identify relevant skills and applying a case study to understand a data scientist’s workflow.
I learned the fundamentals of data science, including how professionals define the field, the diverse career paths available, and the importance of data analysis in solving business problems. The course clarified the skills needed, such as coding, statistical analysis, and communication, and provided practical exposure to data science workflows through case studies and discussions.
While data science is not directly tied to photography, the analytical skills I gained are valuable for image processing and high-quality image/video production. Understanding data-driven decision-making helps me analyze image metadata or user engagement metrics to optimize visual content. Familiarity with tools like Python can be applied to automate image processing tasks, such as batch editing or enhancing video quality, streamlining workflows and improving the precision of my creative outputs.