Learning Data Mining with R
2.5 (3 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
40 students enrolled
Wishlisted Wishlist

Please confirm that you want to add Learning Data Mining with R to your Wishlist.

Add to Wishlist

Learning Data Mining with R

A complete course to help you learn all the relevant aspects of data mining using R
2.5 (3 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
40 students enrolled
Created by Packt Publishing
Last updated 9/2016
English
Current price: $10 Original price: $85 Discount: 88% off
5 hours left at this price!
30-Day Money-Back Guarantee
Includes:
  • 2.5 hours on-demand video
  • 1 Supplemental Resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • 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
View Curriculum
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.
Description

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.
Compare to Other R Courses
Curriculum For This Course
30 Lectures
02:16:54
+
Getting Started – A Motivating Example
5 Lectures 24:00

This video gives an overview of the entire course.

Preview 03:30

The aim of this video is to show how easy it is to use R for data mining. On the other hand, the expectations are set because R is sometimes a bit hard to learn—especially for programmers.

Getting Started with R
05:05

You have to accept that most of your work will involve data cleansing, which is one of the most important steps in data mining. Fortunately, R has all the tools in place to do this task as elegantly as possible.

Data Preparation and Data Cleansing
04:10

The aim of this video is to explain the basic concepts of R. This is exemplified by showing how easy it is to load data in R. Get an idea about how this is done in most of the cases as well as for some special cases such as databases and big data technologies.

The Basic Concepts of R
05:46

This video gives an overview of the data frame object, which is an essential part of R and part of every analysis. You will learn what a data frame is and how to use it for data manipulation.

Data Frames and Data Manipulation
05:29
+
Clustering – A Dating App for Your Data Points
3 Lectures 14:51

We want to explain that data is nothing but points in a multidimensional vector space exemplified by an example.

Preview 03:59

Points in a multidimensional vector space can be drawn and analyzed by introducing k-means—the simplest of the clustering algorithms.

An Algorithmic Approach to Find Hidden Patterns in Data
06:24

Coming from a hard-to-understand dataset, process and visualize it to gain insights.

A Real-world Life Science Example
04:28
+
R Deep Dive, Why Is R Really Cool?
4 Lectures 18:42

The aim of this video is to show how powerful R is as a data language. We will query an internal example dataset and show how it can be filtered and aggregated on.

Preview 04:00

The aim of this video is to show how powerful R is as a data language. Now we concentrate on data types.

R Data Types
05:44

Next, we concentrate on functions and indexing.

R Functions and Indexing
04:14

The aim of this video is to show how object-oriented programming is done in R since some of the algorithms covered rely on it.

S3 Versus S4 – Object-oriented Programming in R
04:44
+
Association Rule Mining
6 Lectures 25:03

The aim of this video is to show a little example to motivate the attendee based on the standard market basket analysis.

Preview 03:09

The aim of this video is to explain the mathematical structure "graph".

Introduction to Graphs
02:09

The aim of this video is to explain the different types of association rules.

Different Association Types
05:27

The aim of this video is to explain the Apriori Algorithm.

The Apriori Algorithm
06:38

The aim of this video is to explain the Eclat Algorithm.

The Eclat Algorithm
03:53

The aim of this video is to explain the FP-Growth Algorithm.

The FP-Growth Algorithm
03:47
+
Classification
5 Lectures 21:10

This video introduces the discipline of classification, the mathematical foundation for understanding Bayes' theorem and the Naïve Bayes classifier.

Preview 06:00

Now since we've understood Bayes' theorem, we can derive the Bayes classifier and use naïve Bayes for spam classification in R.

The Naive Bayes Classifier
03:50

This is a practical example of using naïve Bayes for spam classification in R

Spam Classification with Naïve Bayes
03:32

Introduction to support vector machines, understanding how to use them to separate points in multidimensional vector spaces, and finally using kernels in non-linearly separable data

Support Vector Machines
04:28

Introduction to lazy learning using k-nearest neighbors. This video explains how KNNs work and how they are applied in R

K-nearest Neighbors
03:20
+
Clustering
4 Lectures 18:14

This video introduces the discipline of hierarchical clustering.

Preview 05:44

This video introduces the discipline of distribution based clustering.

Distribution-based Clustering
06:54

This video introduces the discipline of density based clustering.

Density-based Clustering
03:11

A practical example of using DBSCAN in R.

Using DBSCAN to Cluster Flowers Based on Spatial Properties
02:25
+
Cognitive Computing and Artificial Intelligence in Data Mining
3 Lectures 14:54

This video introduces neural networks.

Preview 06:09

This video shows an example in R—how to use the H2D deep learning framework for handwritten digit recognition (classification).

Using the H2O Deep Learning Framework
02:28

This video shows an example in R—how to use the H2D deep learning framework for anomaly detection of real-time Iot sensor data.

Real-time Cloud Based IoT Sensor Data Analysis
06:17
About the Instructor
Packt Publishing
3.9 Average rating
8,229 Reviews
58,938 Students
687 Courses
Tech Knowledge in Motion

Packt has been committed to developer learning since 2004. A lot has changed in software since then - but Packt has remained responsive to these changes, continuing to look forward at the trends and tools defining the way we work and live. And how to put them to work.

With an extensive library of content - more than 4000 books and video courses -Packt's mission is to help developers stay relevant in a rapidly changing world. From new web frameworks and programming languages, to cutting edge data analytics, and DevOps, Packt takes software professionals in every field to what's important to them now.

From skills that will help you to develop and future proof your career to immediate solutions to every day tech challenges, Packt is a go-to resource to make you a better, smarter developer.

Packt Udemy courses continue this tradition, bringing you comprehensive yet concise video courses straight from the experts.