Presenters are seasoned tech leaders who can deliver a leading-edge keynote, workshop, or panel on an emerging trend in the data economy. Case studies or demonstrations of technology are welcome. Sessions are 45 minutes long and technical in nature. Slidedecks are optional, but preferred.

Example session topics could include, but are not limited to:

• Emerging Trends in Data-Driven Product Development: Explore how to leverage real-time data for smarter product iteration and innovation. This workshop will feature tools like Jupyter Notebook, Apache Spark, and Tableau to enhance product workflows, highlight programming languages such as Python, R, and SQL, and apply agile product development methodologies.
• AI Ethics and Responsible Data Usage: Gain insight into best practices for ethical AI implementation and responsible data governance. Participants will work with IBM Watson OpenScale for bias detection and learn about Explainable AI (XAI) using Python libraries like LIME. The session will include ethical review protocols, transparency techniques, and fairness assessments.
• Advanced Data Visualization Techniques: Master the art of transforming complex datasets into actionable insights. This session will showcase D3.js, Power BI, and Plotly for impactful visuals, focus on programming languages like JavaScript and Python, and teach methods for creating interactive dashboards and real-time data updates.
• Building Scalable Data Pipelines: Learn to architect efficient and scalable data flows. The workshop will feature tools like Apache Kafka, AWS Data Pipeline, and Apache Airflow, cover languages such as Python, Scala, and SQL, and delve into ETL processes, stream processing, and microservices architecture.
• The Future of Data Security and Privacy: Navigate new global regulations and best practices in data protection. Attendees will explore tools like Splunk for threat monitoring and Snort for intrusion detection, use Python and PowerShell for scripting, and review zero-trust architecture and encryption strategies.
• Machine Learning Operations (MLOps) Best Practices: Enhance the deployment and lifecycle management of machine learning models. This workshop includes demonstrations of MLflow and Kubeflow, leverages Python and YAML, and discusses automated model retraining, version control, and deployment monitoring.
• The Power of Predictive Analytics: Harness predictive modeling for improved decision-making. Attendees will use tools such as Azure Machine Learning and Scikit-learn, work with languages like Python and R, and apply methods like regression analysis and time series forecasting.
• Data Democratization and Collaboration: Bridge the gap between technical and non-technical teams for better data accessibility. This session highlights tools like Snowflake and Looker, covers SQL and DAX for Power BI, and discusses data cataloging and collaborative dashboard creation.
• AI-Powered Code Review and Debugging Tools: Discover how AI optimizes the code review process. This workshop will introduce tools like DeepCode and GitHub Copilot, work with Java, Python, and C++, and explore methods for automated code review and static code analysis.
• Cybersecurity Threat Intelligence for Developers: Integrate security insights into development workflows. Participants will learn to use Nessus for vulnerability scanning and OWASP ZAP for web app security, write custom Python scripts, and apply secure coding practices and continuous threat monitoring.
• Integrating Geospatial Analytics with Mainstream Development: Explore the use of geospatial data for innovative solutions. This workshop covers tools like ArcGIS and PostGIS, languages such as Python and SQL, and methods for spatial analysis and interactive mapping.
• Enhancing User Experience with Data-Driven Design: Use analytics to drive engaging user interfaces. The session includes tools like Hotjar and Adobe XD, focuses on JavaScript and HTML, and teaches methods for heatmap analysis, user journey mapping, and A/B testing.
• Sustainability in Tech: The Role of Data: Utilize data for greener development practices. Attendees will use carbon footprint calculators and environmental impact platforms, leverage Python and R, and learn methods for lifecycle analysis and energy usage monitoring.
• Agile Data Project Management: Adapt Agile methodologies for data-centric projects. This workshop includes tools like Jira and Trello, focuses on the application of the Scrum framework, and covers sprint planning and retrospective techniques.
• Cloud-Native Data Infrastructure: Optimize cloud services for data storage and processing. This session highlights AWS S3, Google BigQuery, and Apache Cassandra, uses SQL and Python, and teaches methods for cloud-native development and containerized data services.