One of the best-known headlines The Economist has printed over the recent years said the data has replaced oil as the most valuable resource in the world. It’s no surprise, really: collecting and analyzing data allows you to come up with creative insights, valuable solutions, and innovations. It’s also a great way to understand the needs of your consumers and the issues they experience.
In this RapidMiner tutorial, you will learn how to use RapidMiner for data mining. Start the course now, and find out how to improve the speed, quality, and efficiency of your business process!
Let’s start our RapidMiner tutorial by getting the basics down. First and foremost – what is data mining? For a beginner, the name might seem confusing. At first glance, it looks like your mining something for data, right? Wrong!
In a vast sea of data, it’s rather hard to notice a pattern or a tendency that appears repeatedly. This is where data mining, also known as knowledge discovery, comes in. To put it simply, it means analyzing the data itself to draw valuable conclusions and use them as a base for future predictions. As you learn data mining, you will be able to quickly see opportunities:
Choosing a good data mining course might be tricky, as some of them only concentrate on the theory. Of course, getting the fundamentals down before you get your hands dirty is crucial. However, you won’t be able to use them until you also learn how to use specialized data mining software. With my RapidMiner tutorial, you will not only get familiar with the concepts and principles but also find out how to use RapidMiner for data mining on your own.
Skimming through the description of one data mining course after another before you make a choice, you will notice there’s a lot of systems you could use for data mining, such as Echosec, Octoparse, or PolyAnalytic. Why pick a RapidMiner tutorial over them?
The most important reasons include:
As of now, RapidMiner has over half a million active users all around the world. This includes thousands of organizations, universities, and businesses.
For this RapidMiner tutorial, we will be using the RapidMiner Studio version, which allows us to analyze, cleanse, and validate any type of data. I will also explain how you can use the modeling features to the fullest and evaluate the data in an efficient manner. In just under two hours, you will learn how to extract the crucial information to be used in predictive analytics!
Eric Goh is a data scientist, software engineer, adjunct faculty and entrepreneur with years of experiences in multiple industries. His varied career includes data science, data and text mining, natural language processing, machine learning, intelligent system development, and engineering product design. He founded SVBook and extended it with DSTK.Tech and EMHAcademy. DSTK.Tech is where Eric develops his own DSTK data science software. Eric also publishes "Learn R for Applied Statistics" at Apress, and published 5 books at LeanPub and SVBook. He teaches the content at Udemy and EMHAcademy. During his free time, Eric is also an adjunct faculty at Universities and Institutions.
Eric Goh has been leading his teams for various industrial projects, including the advanced product code classification system project which automates Singapore Custom’s trade facilitation process, and Nanyang Technological University's data science projects where he develops his own DSTK data science software. He has years of experience in C#, Java, C/C++, SPSS Statistics and Modeller, SAS Enterprise Miner, R, Python, Excel, Excel VBA and etc. He won Tan Kah Kee Young Inventors' Merit Award and Shortlisted Entry for TelR Data Mining Challenge.
He holds a Masters of Technology degree from the National University of Singapore, an Executive MBA degree from U21Global (currently GlobalNxt) and IGNOU, a Graduate Diploma in Mechatronics from A*STAR SIMTech (a national research institute located in Nanyang Technological University), Coursera Specialization Certificate in Business Statistics and Analysis (Excel) from Rice University, IBM Data Science Professional Certificate (Python, SQL), and Coursera Verified Certificate in R Programming from Johns Hopkins University. He possessed a Bachelor of Science degree in Computing from the University of Portsmouth after National Service. He is also an AIIM Certified Business Process Management Master (BPMM), GSTF Certified Big Data Science Analyst (CBDSA), and IES Certified Lecturer.
Specialties: Data Science, Text Mining, Social Network Analysis, Natural Language Processing, Machine Learning, Software Engineering, Mechatronics, Business.