Session Outline

Statistics show that we have a major shortfall of data scientists but the convergence of engineering and data science, through the invention of citizen data scientists, will help bridge this gap. When compared to data scientists, engineers are relatively plentiful. According to the EU, there are 3 million mechanical engineers in Europe alone. More importantly, by definition, mechanical engineers have an aptitude for mathematics and are familiar with interpreting data. Both are core skills for any aspiring data scientist. What engineers traditionally lacked was coding skills but the invention of low-code / no-code data science solutions have changed all that.

Key Takeaways

  • Early adopters are already reaping the benefits of the convergence between data, AI and engineering. We cannot wait any longer by complaining about resource and budget constraints. We need to start small with low-complexity, high value, high visibility use cases. 
  • We will learn how Altair clients like Ford and Rolls Royce are pioneering the use of low-code machine learning solutions to upskill their engineers and democratise the power of this technology enterprise-wide
  • By drawing on Altair’s pioneering Future Says Series, you will also learn what leading voices in the Nordic data science community believe are the opportunities and threats AI can pose today
  • Looking further ahead, we will also be talking about how to develop the next generation of engineers focusing on the need to prioritize data science at the heart of their learning journeys.

Speaker Bio

Sean Lang – Data Strategist | Altair
Sean Lang is a Data Strategist at Altair – helping democratise the use of low-code machine learning solutions throughout their client base. He is also founder of Future Says – an interview series where he debates the pressing trends in Artificial Intelligence alongside some of Europe’s leading voices in the field. From his background at both Altair and Kx Systems, Sean is passionate about championing data literacy throughout the enterprise, and he believes in a future where everybody can consider themselves a data scientist.

October 14 @ 11:00
11:00 — 11:20 (20′)

Day 1 | M2 | Analytics and Visualisation