Making Sound Decisions With Data Analytics

Topic 1 – The Four Megatrends Reshaping the Data Landscape
Analyse the four major trends identified by Prof. Thulasidas (volume/variety, consumption, velocity, and technological innovation). For each trend, provide a concrete real-world example from a European or global company and discuss how they collectively redefine the role of data professionals today.

Topic 2 – Analytics Literacy: The New Computer Literacy?
Prof. Thulasidas draws a parallel between the computer literacy required in the early information revolution and the analytics literacy demanded today. To what extent is this comparison valid? What specific skills should a modern data scientist master to be considered “analytics literate”?

Topic 3 – Data-Driven vs. Business-Driven Decision Making: Friends or Foes?
The professor argues that data-driven systems and business expertise should complement, not replace, each other. Using concrete industry examples (e.g., dynamic airline pricing, Amazon logistics), build and defend a framework showing how both approaches can be effectively combined in a professional setting.

Topic 4 – The Race to the Bottom: Can Data Science Avoid the Fate of Computer Science?
Prof. Thulasidas warns that data science may follow the same path as computer science, where a flood of lower-skilled workers blurred professional distinctions. Discuss the risks, identify early warning signs visible today, and propose strategies for M2 Data Science graduates to differentiate themselves.

Topic 5 – Clustering Algorithms and the Challenge of Quality Metrics
The professor’s research focuses on developing quality metrics for clustering algorithms. Explain why evaluating clustering quality is a non-trivial problem, present existing approaches (e.g., silhouette score, Davies-Bouldin index), and discuss the added value of mathematically robust, data-driven methods for practitioners such as marketing analysts.

Topic 6 – The IoT and Wearable Revolution: Opportunities and Ethical Challenges for Data Scientists
The podcast highlights the explosion of data generated by smartphones, smartwatches, and IoT devices. Explore the business opportunities this creates while addressing the ethical and privacy concerns data scientists must navigate, particularly in the context of GDPR in Europe.

Topic 7 – Real-Time Analytics: Meeting the Velocity Requirement
Using examples such as Google Maps or Waze cited in the podcast, examine the technical and organisational challenges of generating near real-time insights. What tools, platforms (Hadoop, cloud solutions), and architectural choices enable organisations to meet this velocity requirement?

Topic 8 – The Soft Skills Gap: Why Communication Makes or Breaks a Data Scientist
Prof. Thulasidas stresses that successful data scientists must bridge technical and business domains through strong communication skills. Analyse the importance of this skill set, identify the most common communication failures in data projects, and propose best practices for presenting complex analytical findings to non-technical stakeholders.