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  • 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.

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.

Course Curriculum

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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  • FREE
  • 10 Days


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