PH Deck

Users seeking to advance their knowledge in data science and AI face challenges in standing out among competitors and enhancing their skills in a data-oriented work environment.

Drawbacks: Limited opportunities to gain a competitive edge, struggle to excel in data-oriented roles regardless of experience level.

Limited access to comprehensive data science learning resources for Kathmandu University students in Nepal

Data science tasks require specialized knowledge, which makes it difficult for non-experts to perform advanced analytics. This leads to barriers in leveraging data-driven insights for business or personal use, resulting in missed opportunities and slower decision-making processes. The specialized knowledge requirement is a significant drawback.

Users seeking data science education face limited access to comprehensive, flexible, and project-based courses in Coimbatore, relying on outdated or theoretical-heavy programs with inadequate hands-on training and inflexible schedules.

Users face challenges navigating the unstructured and overwhelming learning path for data science, requiring them to manually gather scattered resources and self-design a curriculum, leading to inefficiency and confusion.

Users rely on manual data analysis requiring coding skills and setup time, leading to slow, complex, and less accessible insights.

Business executives struggle to quickly retrieve and understand key performance indicators (KPIs), charts, and insights from their data, leading to slow decision-making and inefficiencies in business operations. retrieve and understand key performance indicators (KPIs), charts, and insights from their data quickly

Businesses struggle to handle and make sense of massive real estate datasets, leading to inefficiencies and missed opportunities in predictions, strategies, and data interactions.

Drawbacks include the inability to efficiently transform overwhelming amounts of data into actionable insights and the need for significant time and resources to manually analyze satellite and real estate data.

Users rely on traditional analytics tools like Google Analytics which require manual data analysis and interpretation, leading to time-consuming processes and difficulty extracting actionable insights for e-commerce businesses.

Users struggle with managing and querying unstructured data using traditional data warehouses, which require complex Python scripts. The inability to handle unstructured data easily and the need for technical script writing are significant drawbacks.