R Data Analysis Projects
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R Data Analysis Projects

Get valuable insights from your data by building data analysis systems from scratch with R
1.5 (2 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
12 students enrolled
Created by Packt Publishing
Last updated 3/2018
English
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Current price: $86.99 Original price: $124.99 Discount: 30% off
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This course includes
  • 4 hours on-demand video
  • 1 downloadable resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Build end-to-end predictive analytics systems in R
  • Study an experimental design to gather data and conduct analysis
  • Implement a recommender system from scratch using different approaches
  • Use and leverage RShiny to build reactive programming applications
  • Build systems for varied domains including market research, network analysis, social media analysis, and more
  • Explore various R Packages such as RShiny, ggplot, recommenderlab, dplyr, and find out how to use them effectively
  • Communicate modeling results using Shiny Dashboards
  • Perform multi-variate time-series analysis prediction, supplemented with sensitivity analysis and risk modeling
Requirements
  • A fundamental understanding of R and the basic concepts of data analysis is all you need to get started with this video.
Description

R offers a large variety of packages and libraries for fast and accurate data analysis and visualization. As a result, it’s one of the most popularly used languages by data scientists and analysts, or anyone who wants to perform data analysis.
This video will demonstrate how you can put to use your existing knowledge of data analysis in R to build highly efficient, end-to-end data analysis pipelines without any hassle. You’ll start by building a content-based recommendation system, followed by building a project on sentiment analysis with tweets.
You’ll implement time-series modeling for anomaly detection and understand cluster analysis for streaming data. You’ll work through projects on performing efficient market data research, building recommendation systems, and analyzing networks accurately, all provided with easy to follow code.
With the help of these real-world projects, you’ll get a better understanding of the challenges faced when building data analysis pipelines, and see how you can overcome them without compromising on the efficiency or accuracy of your systems. The video covers some popularly used R packages such as dplyr, ggplot2, RShiny, and others, and includes tips on using them effectively.
By the end of this video, you’ll have a better understanding of data analysis with R, and will be able to put your knowledge to practical use without any hassle.

About the Author

Gopi Subramanian is a scientist and author with over 18 years of experience in the fields of data mining and machine learning. During the past decade, he has worked extensively in data mining and machine learning, solving a variety of business problems.
He has 16 patent applications with the US and Indian patent offices and several publications to his credit. He is the author of Python Data Science Cookbook by Packt Publishing.

Who this course is for:
  • This video will take you all the way through the practical application of advanced and effective analytics methodologies in R.
Course content
Expand all 37 lectures 04:07:51
+ Association Rule Mining
7 lectures 49:50

This video gives an overview of the entire course.

Preview 03:31

Our first use case is designing a cross-sell campaign for an imaginative retailer.

Understanding the Recommender Systems
06:04
Our first recommendation algorithm is association rule mining.
Association Rule Mining and Cross-Selling Campaign
19:21

In this video, we will introduce a variation of the association
rule mining algorithm, called the weighted association rule mining
algorithm, which can incorporate some of the retailer input in the form
of weighted transactions.

Weighted Association Rule Mining
05:24
Another algorithm we will see is the HITS algorithm.
Hyperlink-Induced Topic Search
06:24

Another variation of association rule mining called negative
association rule mining, which is an efficient algorithm used to find
anti-patterns in the transaction database.

Negative Association Rules
02:46

Our last video of this section will wrap up by introducing package
arulesViz: an R package with some cool charts and graphics to visualize
the association rules, and a small web application designed to report
our analysis using the R Shiny R package.

Rules Visualization and Wrapping Up
06:20
+ Fuzzy Logic Induced Content-Based Recommendation
4 lectures 34:03

Content-based methods rely on the product properties to create
recommendations, they can ignore the user preferences, to begin with.
Content-based method dishes out the Needed recommendation and user
profile can be built in the background.

Preview 06:56

A news aggregator collects syndicated web content such as new
articles, blogs, video, and similar items at a centralized location for
easy viewing.

News Aggregator Use Case and Data
03:25

This video will depict the first step that is building similarity indec.

Designing the Content-Based Recommendation Engine – Similarity Index
09:21
This video will explain the next step of designing that is searching.
Designing the Content-Based Recommendation Engine – Searching
14:21
+ Collaborative Filtering
4 lectures 41:37

Given a database of user ratings for products, where a set of
users have rated a set of products, collaborative filtering algorithms
can give ratings for products yet to be rated by a particular user.

Introduction to Collaborative Filtering
11:44

Recommenderlab package is very useful to get an overview of the recommenderlab package.

recommenderlab Package
04:03
We will use Jester5k dataset to build our recommender system using collaborative filtering.
Collaborative Filtering Use Case and Data
06:37

We will design a recommendation system for suggesting jokes to the users.

Designing and Implementing Collaborative Filtering
19:13
+ Twitter Text Sentiment Classification
6 lectures 25:34
Kernel density estimate techniques help find the underlying probability distribution.
Kernel Density Estimation
05:36

This video will leverage the twitteR package to extract tweets.

Twitter Text and Sentiment Classification
04:15
In this video, we will use a sentiment dictionary to score extracted tweets.
Dictionary Based Scoring
03:58

In this video, we will use the bag-of-words model to represent our text as features.

Text Pre-Processing
05:27

In this video, we are going to build KDE classifier.

Building a Sentiment Classifier
03:22

In this video, RShiny application will search Twitter for a
keyword/hashtag, display the top 100 results and show the sentiment for
the top 100 tweets.

Assembling an R Shiny Application
02:56
+ Record Linkage – Stochastic and Machine Learning Approaches
4 lectures 22:04

Record Linkage - Stochastic and Machine Learning Approaches,
covers the problem of master data management and how to solve it in R
using the RecordLinkage package.

Demonstrating the Use of RecordLinkage Package
07:20
The job of stochastic record linkage is to give a measure of the closeness of the two entities.
Stochastic Record Linkage
05:31

The record linkage problem is modeled as a machine learning problem. It is solved in both unsupervised and supervised manners.

Machine Learning-Based Record Linkage
06:10

In this video, we will load the RLdata500 from the Record Linkage
package and display to the user, implement the weights algorithm and
display the weight range as a histogram and allow the user to select the
lower and upper thresholds of weights for classification.

Building an R Shiny Application – Record Linkage
03:03
+ Streaming Data Clustering Analysis in R
3 lectures 23:56

There is a great demand today to perform analysis on data in
motion, also called streaming data. Streaming data is becoming
ubiquitous with the number of addressable sensors and devices being
added to the internet.

Introducing Stream Clustering
04:56

A data stream is a continuous inflow of ordered points in a
multi-dimensional space. The ordering can be done either explicitly
through timestamps or by some other index.

Introducing the stream Package
11:09
We need a feature in that system to cluster the incoming data and display those clusters in real time in a digital dashboard.
Data Clustering Use Case
07:51
+ Analyze and Understand Networks Using R
4 lectures 23:58

Network analysis is the study of graphs. Graphs are defined by a set of nodes or vertices connected by edges.

Graphs in R
08:33

Category management is analysing a discrete set of similar or
related items sold by a retailer, grouped together, as a strategic
business unit.

Use Case and Data Preparation
07:04

There are two steps in product network analysis. The first step is
to transform the point-of sale data into product pairs and their
transaction frequency.

Product Network Analysis
05:13

In this Rshiny application, first we will load a transaction file,
Calculate the product pairs and their transaction frequency, and
display them.

Building an R Shiny Application – Networks
03:08
+ Taming Time Series Data Using Deep Neural Networks
5 lectures 26:49

A time series is a series of data points indexed in time order.
Financial industries have been using time series data for various
market-related purposes.

Time Series Data
05:45

Deep learning allows us to build sophisticated models, which can
capture non-linear relationships in the data in a much more efficient
and faster manner.

Deep Neural Networks
04:29

This video will use the package MXNet R to build our neural networks.

Introduction to the MXNet R Package
02:36

In this video, we will look at some symbolic declaration. We will
also build a deep neural network model to predict the movement of
certain stock.

Symbolic Programming in MXNet
07:29

We will split our data into training and test datasets.

Training Test Split
06:30