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R Data Analysis Projects

Video Description

Get valuable insights from your data by building data analysis systems from scratch with R

About This Video

  • A handy guide to take your understanding of data analysis with R to the next level
  • Real-world projects that focus on problems in finance, network analysis, social media, and more
  • From data manipulation to analysis to visualization in R, this video will teach you everything you need to know about building end-to-end data analysis pipelines using R

In Detail

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.

Table of Contents

  1. Chapter 1 : Association Rule Mining
    1. The Course Overview 00:03:31
    2. Understanding the Recommender Systems 00:06:04
    3. Association Rule Mining and Cross-Selling Campaign 00:19:21
    4. Weighted Association Rule Mining 00:05:24
    5. Hyperlink-Induced Topic Search 00:06:25
    6. Negative Association Rules 00:02:46
    7. Rules Visualization and Wrapping Up 00:06:20
  2. Chapter 2 : Fuzzy Logic Induced Content-Based Recommendation
    1. Introducing Content-Based Recommendation 00:06:56
    2. News Aggregator Use Case and Data 00:03:26
    3. Designing the Content-Based Recommendation Engine – Similarity Index 00:09:21
    4. Designing the Content-Based Recommendation Engine – Searching 00:14:21
  3. Chapter 3 : Collaborative Filtering
    1. Introduction to Collaborative Filtering 00:11:45
    2. recommenderlab Package 00:04:03
    3. Collaborative Filtering Use Case and Data 00:06:38
    4. Designing and Implementing Collaborative Filtering 00:19:13
  4. Chapter 4 : Twitter Text Sentiment Classification
    1. Kernel Density Estimation 00:05:36
    2. Twitter Text and Sentiment Classification 00:04:15
    3. Dictionary Based Scoring 00:03:58
    4. Text Pre-Processing 00:05:28
    5. Building a Sentiment Classifier 00:03:22
    6. Assembling an R Shiny Application 00:02:56
  5. Chapter 5 : Record Linkage – Stochastic and Machine Learning Approaches
    1. Demonstrating the Use of RecordLinkage Package 00:07:20
    2. Stochastic Record Linkage 00:05:31
    3. Machine Learning-Based Record Linkage 00:06:11
    4. Building an R Shiny Application – Record Linkage 00:03:03
  6. Chapter 6 : Streaming Data Clustering Analysis in R
    1. Introducing Stream Clustering 00:04:56
    2. Introducing the stream Package 00:11:09
    3. Data Clustering Use Case 00:07:52
  7. Chapter 7 : Analyze and Understand Networks Using R
    1. Graphs in R 00:08:33
    2. Use Case and Data Preparation 00:07:05
    3. Product Network Analysis 00:05:14
    4. Building an R Shiny Application – Networks 00:03:09
  8. Chapter 8 : Taming Time Series Data Using Deep Neural Networks
    1. Time Series Data 00:05:46
    2. Deep Neural Networks 00:04:30
    3. Introduction to the MXNet R Package 00:02:36
    4. Symbolic Programming in MXNet 00:07:29
    5. Training Test Split 00:06:31