Join us for this year’s SAP Women in Data Science @ SAP event on May 15th!

SAP Business Women’s Network (BWN) Palo Alto is excited to offer the seventh Women in Data Science @ SAP Conference which is a virtual event focusing on the integral role of Data Science in the global current reality. This year’s theme is “The Power of Data Science and AI for a Better World.”

Over the past 9 years, there has been an increasing demand for data science-related tasks ranging from pure data analysis to AI-driven model design and development. Post COVID-19 there is an increased need for data-driven tools to help in decision-making and revise many analytics and AI-driven prediction systems by studying new patterns not seen before. Hence, setting a new benchmark and level of expectation across different industries. Today, we know that actionable insights from data science projects can take different forms. They could range from being a product itself, an application, a report, etc. Data science skills are necessary in development as well in sales, marketing, and strategic planning teams. As the workplace shifts to a hybrid approach, WiDS @ SAP aims to bring together practitioners in development, product design, sales, marketing, and academia to talk about transforming data into valuable insights, capitalize on competitive advantage and make our work environment fairer, ethical and sustainable.

The conference will feature both live and on-demand sessions from the following categories:

Sustainability

Sustainability is often seen through the lens of ESG – Environmental, Social and Governance. The UN has developed 17 sustainable development goals (SDGs) that dive deeper into core pillars of ESG. Coupled with innovations and new technologies, what are new applications and means from data science which can advance the goals put forth by the UNSDGs? How can data science enable more sustainable practices – applied for the benefit of a better tomorrow, not just for profit?

Automated Machine Learning (AutoML)

By using ML for ML itself, AutoML is automating the repetitive tasks by building techniques to automatically solve new use cases, without the need for human ML experts. This is helping the “democratization” of machine learning. This topic will focus on how the Data Scientists are using Auto ML frameworks for deploying models, studying the model performance, visualizing the data, etc.

Data Driven Experience (Personalization)

In a post pandemic world where e-commerce and digital experiences are becoming a norm, there is a great potential for more personalized experiences. Every aspect of an engagement can be measured and analyzed which leads to the question: How can Data Science leverage this customer data to ensure an enjoyable, engaging, and personalized user experience?

AI (including GenAI, AI and Ethical AI)

Artificial Intelligence models which can generate human-like content, for example the chatbot: ChatGPT and the image generator: DALL-E have taken the world by storm. From code snippets to customer support, to customized advertisements, these technologies are causing a major disruption. What would be their long-term impact on the society? What do we need to be careful about? How would we make sure they are not being misused?

Data Visualization

Data Visualization, which is the graphical representation of information, is one of the most effective means of conveying information and storytelling. How can Data Visualization compel action – to move beyond just static charts to augmented analytics embedded where people work? How can it, in combination with social media, cause real impact?

Data Governance & Security

Data is everywhere and often dubbed as an engine for growth. Data points are prevalent in daily routines – in the form of traffic sensors, government services, sports & entertainment insights, or geolocation tagging services – and the potential for Big Data to predict trends and model future scenarios generates vast insights, enabling data driven decision making. As the volume of data increases exponentially there is a need for data governance and security. As global data volume rises – so too does the potential for data breaches or disruptive cyber-attacks by bad actors. If cybercrimes were measured as an economic state – it would rank third, after the economies of US and China. How do applied data science and machine learning make room for progress in cybersecurity? What are skills required to fill an organization demands, in tomorrow’s digital world?

Other Challenges in Data Science

Data scientists encounter challenges at each step of their process. There are many more challenges than the ones mentioned in the categories above, so we encourage you to submit your abstract even if it doesn’t fit into any of the above categories.