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Data mining is a growing demand on the market as the world is generating data at an increasing pace. R is a popular programming language for statistics. It can be used for day-to-day data analysis tasks.
Data mining is a very broad topic and takes some time to learn. This course will help you to understand the mathematical basics quickly, and then you can directly apply what you’ve learned in R. This course covers each and every aspect of data mining in order to prepare you for real-world problems. You’ll come to understand the different disciplines in data mining. In every discipline, there exist a variety of different algorithms. At least one algorithm of the various classes of algorithms will be covered to give you a foundation to further apply your knowledge to dive deeper into the different flavors of algorithms.
After completing this course, you will be able to solve real-world data mining problems.
About The Author
Romeo Kienzler is a Chief Data Scientist at the IBM Watson IoT Division. In his role, he is involved in international data mining and data science projects to ensure that clients get the most out of their data. He works as an Associate Professor for data mining at a Swiss University and his current research focus is on cloud-scale data mining using open source technologies including R, ApacheSpark, SystemML, ApacheFlink, and DeepLearning4J. He also contributes to various open source projects. Additionally, he is currently writing a chapter on Hyperledger for a book on Blockchain technologies.
What are the requirements?
- This course is ideal for data analysts and scientists with a basic knowledge of R libraries who would like to explore R’s potential to mine data.
What am I going to get from this course?
- Get to know the basic concepts of R: the data frame and data manipulation
- Discover the powerful tools at hand for data preparation and data cleansing
- Visually find patterns in data
- Work with complex data sets and understand how to process data sets
- Work with complex data sets and understand how to process data sets Get to know how object-oriented programming is done in R
- Explore graphs and the statistical measure in graphs
- Gain insights into the different association types
- Decide what algorithms actually should be used and what the desired and possible outcomes of the analysis should be
- Grasp the discipline of classification, the mathematical foundation that will help you understand the bayes theorem and the naïve bayes classifier
- Delve into various algorithms for classification such as KNN and see how they are applied in R
- Evaluate k-Means, Connectivity, Distribution, and Density based clustering
Who is the target audience?
- Through the course, you will come to understand the different disciplines of data mining using hands-on examples where you actually solve real-world problems in R. For every category of algorithm, an example is explained in detail including test data and R code
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