LA4-120
Sessions in LA4-120
The Story Behind the Numbers
How can data be both right and wrong? This talk emphasizes the importance of aligning data analysis with business context and effective communication to enable confident decision-making while preventing reputational damage and financial oversights.
Towards Trustworthy Data Collaborations in GenAI Era
This talk explores Trustworthy Data Collaboration in the GenAI era, focusing on how Data Clean House enables secure data sharing across industries such as digital marketing, finance, and healthcare. With GDPR compliance and privacy-preserving technologies like confidential computing, it ensures collaborations without compromising data privacy.
Out of the Ivory Tower and into the Cloud: Driving Innovation in Business Analytics Education
Drawing from both academic and industry experience, this session will show how integrating experiential learning with cloud technologies can better equip early-career data professionals with diverse skill sets while generating value for organizations through partnerships. Traditionally, coursework has focused on specific skills (e.g., Python, SQL) with a fragmented approach to business applications and technologies. Leveraging cloud platforms allows educators and professionals to create a more cohesive, seamless pathway that delivers tangible benefits.
How to Land a Data Analytics Job in 2024/2025
In this talk, discover essential strategies for securing a data analytics job in 2024/2025. Learn about in-demand skills, effective Linkedin tips, and networking techniques. Explore the importance of hands-on projects and certifications, as well as insights into industry trends to enhance your employability and stand out in a competitive job market.
The Impact of R/R Shiny in Finance
R/R Shiny is rapidly gaining significant traction in the finance industry due to its powerful features and capabilities. There are many R packages designed for financial data analysis and visualization. They make it easy to import, clean and manipulate data. They also support performing complex calculations, statistical analyses, and generating insightful, beautiful visualizations to gain insights into market trends, risk management, and portfolio optimization. Time series analysis and machine learning are also much easier to handle in R.