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.
Learning the Basics of RapidMiner In this module we're going to start off by learning the basics of the RapidMiner software. First, we'll run through the steps to install and set up the RapidMiner software on your machine, which is a nice and simple process. We will then introduce the general user interface of RapidMiner and show how it can be customized to suit your specific needs or preferences. Some commonly used extensions will be introduced, such as text processing, Python and R integration, web mining, and more. We will introduce the data visualization interface and learn how this can be customized, and finally, we will discuss some of the data visualization options.
Preparing the Data In this module we're going to dive even deeper into using the RapidMiner software for data science processes. Specifically, we're going to look into preparing the data, which is one of the critical first steps for data processing. First, we will run through how to import data into RapidMiner Studio. We will then learn how the data is held and stored. We will also learn how data can specifically be read, viewed, and understood within the software, and some basic data preparation methods will be introduced, such as filtering, sorting, merging, and grouping of data, and each of these operators can be extremely useful in properly preparing data for processing and classification. Finally, we will discuss how we can connect databases with RapidMiner Studio.
Analyzing the Data and Further Learning In this module we're going to dive deeper into using the RapidMiner software for data science processes. Specifically, we're going to look into analyzing the data, as well as discussing some next steps and further learning. First, we will introduce modeling within RapidMiner Studio. We will then learn how to classify data and why this is useful for us. We will briefly introduce some more advanced topics, and finally, we will discuss further learning opportunities and next steps to improve your knowledge of data science and RapidMiner even further.