Altair Data Science Day | November 2, 2023

APAC Session: 2:30 - 2:50 PM (IST)

AMER Session: 12:00 - 12:20 (EST)

Speaker: Jim Ryan, VP Global Academic Program

Abstract: This event, and especially this introductory session, is intended to show our appreciation for the impressive work and many contributions of our growing international community of Citizen Data Scientists using Altair software to achieve their goals.

It will provide a big-picture overview of the day’s sessions and some highlights from customer stories involving data analytics or AI/ML.  Along the way, we’ll point out some handy resources available online (especially for the Altair RapidMiner software) intended to help new users get started and experienced users expand their knowledge and perhaps obtain certification.

APAC Session: 2:50 - 3:10 PM (IST)

AMER Session: 12:20 - 12:40 PM (EST)

Speaker: Prof. Amit Deokar - UMass Lowell

Abstract: Organizations are keen on leveraging data as an asset, building their analytics capabilities, and ultimately deriving value for better decision-making. Also, recent AI transformation has brought renewed focus on machine learning techniques for analytics. This talk will focus on how RapidMiner can be used as a powerful analytics platform in courses for teaching and learning about machine learning techniques. With business analytics applications, the talk will emphasize the ability to learn and develop analytics pipelines in a low-code, no-code manner with RapidMiner, while also having the ability to enhance these pipelines with coding as and when needed.

APAC Session: 3:10 - 3:30 PM (IST)

AMER Session: 12:40 - 1:00 PM (EST)

Speaker: Prof. Andreas Seufert - Ludwigshafen University of  Business & Society

Abstract: Course Instructors in Data Science face the challenge of preparing students for Digital Transformation. While students usually gain a good insight in e.g. marketing, controlling, or accounting through their courses, there is often the task of training these “domain experts” on data models for predictive or root cause analysis. 
Prof. Seufert will show how he educates students and industry experts alike with Online Teaching, hands-on exercises, and best-practice use cases. Altair RapidMiner allows for a fast learning curve and enables programming novices, even non-programmers, to build up their data models in the limited time of a lecture.

APAC Session: 3:30 - 3:50 PM (IST)

AMER Session: 1:00 - 1:20 PM (EST)

Speaker: Prof. Mathew North - Utah Valley University

Abstract: In this presentation, Dr. Matt North will discuss using RapidMiner as a teaching and learning tool to empower both current and future generations of data workers who can wrangle and analyze their data quickly, effectively, and insightfully. He’ll demonstrate his online, interactive learning resource, Data Mining for the Masses, which can be employed in environments ranging from traditional classrooms, to on-the-job training workshops, to massively online course environments.

APAC Session: 4:00 - 4:20 PM (IST)

AMER Session: 1:30 - 1:50 PM (EST)

Speaker: Dr. Ingo Mierswa, SVP Product Development Altair, Founder of RapidMiner

Abstract: In this session, you will learn how engineers can leverage their unique skills and domain expertise to solve the problems they face most frequently.

Learn how to break down data silos and maximize the value of the information captured from sensors, machine logs, production records, and more. Finally, we will take a look into the crystal ball ourselves: How are ML and AI going to change how we work in the years to come?

APAC Session: 4:20 - 4:40 PM (IST)

AMER Session: 1:50 - 2:10 PM (EST)

Speaker: Prof. Lee Wells - Western Michigan University

Abstract: I believe that one of the biggest hurdles when educating students in data science is overcoming narrow views of what data is and what data can be. In this session, I will demonstrate an activity that I use in the first lecture for my data mining course. The goal of this activity is to provide a fun and thought-provoking experience that exposes students to data not traditionally discussed in engineering curricula and have them begin thinking about data differently. At the same time, this activity serves the purpose of introducing students to RapidMiner and some of its fundamental uses.

APAC Session: 4:40 - 5:00 PM (IST)

AMER Session: 2:10 - 2:30 PM (EST)

Speaker: Sonal Bihani - Student at University of Waterloo

Abstract: Join me for an insightful journey into the world of cutting-edge data science! As a student intern who successfully harnessed the power of RapidMiner for a transformative Electric Vehicle (EV) Adoption project, I'll provide a glimpse into how this robust platform simplifies intricate data analysis. Discover its capabilities in handling diverse datasets, conducting predictive modeling, and creating visualizations to streamline data exploration and insights. I'll also share my experience with RapidMiner Academy, an invaluable resource for mastering RapidMiner and gaining certification. 
Learn how the right tools can elevate complex data analysis projects, making them accessible and efficient, even for students!

APAC Session: 5:00 - 5:20 PM (IST)

AMER Session: 2:30 - 2:50 PM (EST)

Speaker: Prof. Athanasios G Anastasiou - University of Peloponnese

Abstract: Linear regression is a primary statistical technique designed to model the linear relationship between a dependent variable and a set of input predictors, drivers, or independent variables.
The Dependent Variable should be a scale or continuous variable while the inputs, although traditionally and preferably continuous, can be either categorical or continuous. Regression modeling provides a means of assessing not only the extent to which the outcome of the Dependent Variable can be determined but also the effect of each predictor on that outcome.
RapidMiner provides a range of tools for estimating linear models. This study describes how to use and understand key concepts in relation to finding the best-fitted model by using RapidMiner.