RapidMiner is a widely-used data science and machine learning platform. This course will teach you the basics of using RapidMiner, including basic setup, data visualization and preparation methods, and will briefly introduce some data analysis.
Data Science and Machine Learning are rapidly growing fields that use scientific methods and processes to extract useful knowledge and insights from data. In this course, RapidMiner: Getting Started, you will learn foundational knowledge of solving real-world data science problems. First, you will learn the basics of the software including setup, installation, interface, and data visualization options. Next, you will discover some commonly used data preparation methods. Finally, you will explore how to analyze data in order to come to useful conclusions and insights. When you’re finished with this course, you will have the skills and knowledge of RapidMiner needed to solve data science and machine learning problems.
Justin Flett is a Mechatronics Engineer currently working as a Professor within the Faculty of Applied Science and Technology at Sheridan College. Justin has previously held positions at Hydro One Networks, Ford Motor Company, and ABB Robotics spanning across both the electrical and mechanical engineering industries. Most recently, he has been working as an Product Development Professional specializing in training, services, and consultation nation-wide, ranging from design fundamentals to advanced product development solutions.
Course Overview Hi everyone. My name is Justin, and welcome to my course, RapidMiner: Getting Started. I am a mechatronics engineer, and I'm currently an engineering professor at Sheraton College. Prior to this I have many years of engineering and consulting experience across numerous industries. This course is a getting started course for learning the fundamentals of solving data science problems with the RapidMiner Studio software, so no prior experience is required. Some of the major topics that we will cover include installation and set up of RapidMiner Studio, data preparation methods, analyzing the data to help solve data science problems, and finally, we'll discuss some further learning and next steps. By the end of this course you will be proficient in the fundamental techniques required to solve data science problems with RapidMiner Studio. From here continue your learning by diving into RapidMiner and data science with courses on RapidMiner: Data Science and Machine Learning, RapidMiner: Advanced Techniques, advanced machine learning and advanced data science. I hope you'll join me on this journey to learn RapidMiner and data science fundamentals with the RapidMiner Getting Started course at Pluralsight.