- 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.
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.
- 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.
|Section 1: Getting Started – A Motivating Example|
|1.1 The Course Overview||00:00:00|
|1.2 Getting Started with R||00:00:00|
|1.3 Data Preparation and Data Cleansing||00:00:00|
|1.4 The Basic Concepts of R||00:00:00|
|1.5 Data Frames and Data Manipulation||00:00:00|
|Section 2: Clustering – A Dating App for Your Data Points|
|2.1 Data Points and Distances in a Multidimensional Vector Space||00:00:00|
|2.2 An Algorithmic Approach to Find Hidden Patterns in Data||00:00:00|
|2.3 A Real-world Life Science Example||00:00:00|
|Section 3: R Deep Dive, Why Is R Really Cool?|
|3.1 Example – Using a Single Line of Code in R||00:00:00|
|3.2 R Data Types||00:00:00|
|3.3 R Functions and Indexing||00:00:00|
|3.4 S3 Versus S4 – Object-oriented Programming in R||00:00:00|
|Section 4: Association Rule Mining|
|4.1 Market Basket Analysis||00:00:00|
|4.2 Introduction to Graphs||00:00:00|
|4.3 Different Association Types||00:00:00|
|4.4 The Apriori Algorithm||00:00:00|
|4.5 The Eclat Algorithm||00:00:00|
|4.6 The FP-Growth Algorithm||00:00:00|
|Section 5: Classification|
|5.1 Mathematical Foundations||00:00:00|
|5.2 The Naive Bayes Classifier||00:00:00|
|5.3 Spam Classification with Naïve Bayes||00:00:00|
|5.4 Support Vector Machines||00:00:00|
|5.5 K-nearest Neighbors||00:00:00|
|Section 6: Clustering|
|6.1 Hierarchical Clustering||00:00:00|
|6.2 Distribution-based Clustering||00:00:00|
|6.3 Density-based Clustering||00:00:00|
|6.4 Using DBSCAN to Cluster Flowers Based on Spatial Properties||00:00:00|
|Section 7: Cognitive Computing and Artificial Intelligence in Data Mining|
|7.1 Introduction to Neural Networks and Deep Learning||00:00:00|
|7.2 Using the H2O Deep Learning Framework||00:00:00|
|7.3 Real-time Cloud Based IoT Sensor Data Analysis||00:00:00|
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