Altair Data Science Day 2023

Exploring code-free and code-friendly learning & teaching

The current and projected shortage of data scientists is well known: In an Altair industry survey, 75% of respondents say they already struggle to find enough data science talent, while the number of data scientist roles is projected to grow 35% from 2022 to 2032 - much faster than the average for all occupations. Universities, students, and companies are all seeking ways to address this issue.

Watch our event on demand to understand how others in the Altair RapidMiner community have leveraged this tool’s broad applicability to make more informed, data-driven decisions – by including data science and analytics in numerous university and open online courses pertaining to a wide range of domains such as business, finance, science, healthcare, and engineering.

Follow presentations and discussions on how the low-code approach of Altair RapidMiner facilitates the education of novices in data science with a low barrier to entry. Hear from Altair leaders regarding the present and future focus on using the RapidMiner platform for Engineering Data Science.


Fill out the form on the right and watch the whole event or your hand-picked sessions immediately!


The live event was held on November 2nd, 2023 but all recordings are now available to watch on demand. Fill out the form on the right and get immediate access.


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 provides 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 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.

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.

Speaker: Prof. Pankaj Vyas - Manipal University Jaipur

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.

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?

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.

Speakers: Prof. Shaila S.G and Prof. Monish L - Dayananda Sagar University

Abstract: In this presentation, we will highlight: 
1. Domains of Data Science
2. Data Science Talent Dearth
3. Benefits of Academia and Industry Connections
4. Challenges Faced by Students
5. Effective Solution by Altair
6. A Success Story of Dayananda Sagar University

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.

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.

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!

Speaker: Prof. Gururaja H S and his student Laksh Cowlessur - BMS College of Engineering

Abstract: This presentation will dive deep into the need for a strong AI Centre of Excellence (COE) in an academic organization and also emphasize the benefits it provides in guiding learners on their path to learning this vast domain in a more systematic manner. 


Journey of embarking on real-time AI projects with professor guidance, modern tools, and industry expertise

In the second part of this presentation, Prof. Gururaja's student Laksh Cowlessur will elaborate on the process of creating useful AI systems under the guidance of a professor and also talk about moving to modern tools that facilitate an intuitive understanding of AI program development thereby, making it more interesting and easier to grasp. 


APAC Session: Rahul Ponginan & Sreejay Sreedharan

AMER Session:Ingo Mierswa, Jim Ryan & Scott Genzer

Abstract: In this session, our Altair experts will answer questions that were submitted throughout the event as well as during this session itself. Join us for this last part of the Data Science Day to clear all remaining question marks you might still have in your head and potentially win prizes for staying until the end*.


*Winners will be picked randomly from participants of this session at the very end.

About the Event


Who is this for?

This event is for anyone wanting to complement their learning and/or teaching of data science by using Altair RapidMiner. No matter if you are a new or existing professor already teaching with RapidMiner, a university student wanting to learn data science to be better prepared for a future job, or if you are working in industry and seeking ideas for what is possible with RapidMiner in your domain. 


Why should I care?

  • Become part of the global Altair RapidMiner community with 1M+ users and growing 
  • Explore ways to develop your own skills with RapidMiner to become a more proficient citizen data scientist
  • Know how to access and make good use of academic resources available, especially for course instruction to help your students and learners effectively gather insight from data and use it to positively shape the future
  • Learn from data science course instructors and Altair experts worldwide 
RM Courses Image no title

What is RapidMiner?

Altair RapidMiner is a code-free and code-friendly platform for Data Science, Artificial Intelligence (AI), and Machine Learning (ML). To explore what can be done with it and to use it for free on any non-commercial task or application, download the free Personal Edition (find a download link at the end of this page).

Our Speakers

Amit Deokar 2

Prof. Amit Deokar

Associate Dean of Undergraduate Programs, Manning School of Business

University of Massachusetts Lowell

Prof. Vyas

Prof. Pankaj Vyas

Professor and Head of Department of Information Technology

Manipal University Jaipur


Prof. Andreas Seufert

Professor, Director of Business Innovation Lab, Chair of Business Administration, especially Information Management

Ludwigshafen University of Business & Society

Lee Wells

Prof. Lee Wells

Associate Professor, Department of Industrial and Entrepreneurial Engineering and Engineering Management

Western Michigan University

Shaila SG

Prof. Shaila S. G

Professor and Chairperson, Department of CSE (Data Science)

Dayananda Sagar University

Monish L

Prof. Monish L

Assistant Professor, Department of Computer Science & Engineering

Dayananda Sagar University

Athanasios G Anastasiou 2

Prof. Athanasios G Anastasiou

Associate Professor, Economics of Development and Growth

University of Peloponnese

Matt North

Prof. Matthew North

Professor and Program Coordinator, Information Systems

Utah Valley University


Sonal Bihani

Student, Master of Data Science and Artificial Intelligence (MDSAI)

University of Waterloo

Prof Gururaja H S

Prof. Gururaja H S

Assistant Professor, Department of Information Science and Engineering

BMS College of Engineering

Laksh C

Laksh Cowlessur

Bachelor's Student Electrical and Electronics Engineering

BMS College of Engineering


Dr. Ingo Mierswa

SVP Product Development, Founder of RapidMiner


Your Host

Jim Ryan

Jim Ryan

VP Global Academic Program


Ready to Get Started with Data Science?

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