Megan Lieu, a prominent figure in the data science community, discusses the importance of collaboration in data science teams, creating a SQL course for finance professionals, the value of building in public, and overcoming fear of public speaking.
Collaboration between data teams and other departments is crucial for aligning data initiatives with organizational objectives.
Providing resources and promoting data literacy among citizen data scientists enables effective collaboration with data teams.
Deep dives
The Importance of Collaboration for Data Teams
Collaboration within a data team is crucial, as no data effort is a single person's effort. It is important for data teams to collaborate with their organization outside of the team to align data initiatives with the organization's main metrics and objectives. This collaboration requires the data team to convince the rest of the organization of the value and relevance of their data initiatives. In addition, there is a need for the outside organization to communicate back to the data team, creating a two-way channel of communication. This is particularly challenging when dealing with citizen data scientists, individuals who may not have formal data training but are required to interact with data as part of their everyday jobs. Providing resources and promoting data literacy among citizen data scientists is key to enabling effective collaboration between data teams and other departments.
Creating a Course on SQL for Finance
The podcast discusses the creation of a course on SQL for finance. The course was created based on the need identified by the speaker during their time as a financial analyst. The speaker wished they had access to SQL earlier in their career to build financial models and analyze financial data. The course aims to bridge the gap between finance professionals who were not taught database design and query concepts and the practical application of SQL in a finance setting. The course covers the fundamentals of SQL and also includes real-world examples shared by professionals working in the finance sector. The course is designed to help learners apply SQL to finance-related tasks and gain a deeper understanding of the concepts taught.
The Role of Collaboration in Data Science
The speaker emphasizes the importance of collaboration in data science teams. They reflect on their experiences in different companies, where data varied in importance and integration into decision-making. They found that collaboration was a key differentiator between companies that did not prioritize data and those where data was embedded into the culture. Collaboration within a data science team is essential, but it is also crucial for data teams to collaborate with other departments and stakeholders in the organization. This includes making their data initiatives discoverable and aligning them with the organization's objectives. The speaker highlights the need for a two-way channel of communication between data teams and other departments to ensure effective collaboration.
The Impact of AI Tools on Collaboration
The podcast explores the role of AI tools in enhancing collaboration among data teams and citizen data scientists. The speaker discusses the introduction of AI features in their product, which has received positive feedback from junior team members and non-technical individuals. AI tools have the potential to boost productivity and enable citizen data scientists to work at the same level as traditionally trained data scientists. These tools provide a common interface for collaboration, bridging the gap between technical and non-technical team members. The speaker believes that AI tools will elevate the industry by increasing the baseline level of data proficiency across organizations.
In the latest episode of the Data Bytes Podcast, we had the pleasure of hosting Megan Lieu, a prominent figure in the data science community. Currently working at Deepnote and as an instructor at LinkedIn Learning, Megan brings a wealth of experience from her diverse roles in data science and analytics.