R Data Analysis - Time-Series and Social Media
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R Data Analysis - Time-Series and Social Media

Master this practical approach to performing analytical operations
0.0 (0 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.
0 students enrolled
Created by Packt Publishing
Last updated 9/2017
English
Current price: $10 Original price: $125 Discount: 92% 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?
  • Extract patterns from time-series data and use them to produce forecasts based on them
  • Learn how to extract actionable information from social network data
  • Implement geospatial analysis
  • Present your analysis convincingly through reports and build an infrastructure to enable others to play with your data
View Curriculum
Requirements
  • A basic knowledge of the R programming language is expected.
Description

Data analysis has recently emerged as a very important focus for a huge range of organizations and businesses. R makes detailed data analysis easier, by making advanced data exploration and insight accessible to anyone interested in learning it. This course's hands-on approach will help you perform data analysis. You will learn to perform social network analysis, to uncover hidden insights and trends from data. Later you will perform geospatial analysis to bring data into action with the easy-to-follow examples featured in the video course. By the end of this course, you will mastered quickly adapting the example code for your own needs, thus saving yourself the time-consuming task of constructing code from scratch.

About the Author

Viswa Viswanathan is an associate professor of Computing and Decision Sciences at the Stillman School of Business in Seton Hall University. After completing his PhD in Artificial Intelligence, Viswa spent a decade in Academia and then switched to a leadership position in the software industry for a decade. During this period, he worked for Infosys, Igate, and Starbase. He embraced Academia once again in 2001.

Viswa has taught extensively in diverse fields, including operations research, computer science, software engineering, management information systems, and enterprise systems. In addition to teaching at the university, Viswa has conducted training programs for industry professionals. He has written several peer-reviewed research publications in journals such as Operations Research, IEEE Software, Computers and Industrial Engineering, and International Journal of Artificial Intelligence in Education. He authored a book entitled Data Analytics with R: A Hands-on Approach.

Viswa thoroughly enjoys hands-on software development, and has single-handedly conceived, architected, developed, and deployed several web-based applications.

Apart from his deep interest in technical fields such as data analytics, Artificial Intelligence, computer science, and software engineering, Viswa harbors a deep interest in education, with a special emphasis on the roots of learning and methods to foster deeper learning. He has done research in this area and hopes to pursue the subject further.

Viswa would like to express deep gratitude to professors Amitava Bagchi and Anup Sen, who were inspirational during his early research career. He is also grateful to several extremely intelligent colleagues, notably Rajesh Venkatesh, Dan Richner, and Sriram Bala, who significantly shaped his thinking. His aunt, Analdavalli; his sister, Sankari; and his wife, Shanthi, taught him much about hard work, and even the little he has absorbed has helped him immensely. His sons, Nitin and Siddarth, have helped with numerous insightful comments on various topics.

Shanthi Viswanathan is an experienced technologist who has delivered technology management and enterprise architecture consulting to many enterprise customers. She has worked for Infosys Technologies, Oracle Corporation, and Accenture. As a consultant, Shanthi has helped several large organizations, such as Canon, Cisco, Celgene, Amway, Time Warner Cable, and GE among others, in areas such as data architecture and analytics, master data management, service-oriented architecture, business process management, and modeling. When she is not in front of her Mac, Shanthi spends time hiking in the suburbs of NY/NJ, working in the garden, and teaching yoga.

Shanthi would like to thank her husband, Viswa, for all the great discussions on numerous topics during their hikes together and for exposing her to R and Java. She would also like to thank her sons, Nitin and Siddarth, for getting her into the data analytics world.

Who is the target audience?
  • This course is for anyone who wants to learn analytical techniques from scratch.
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Curriculum For This Course
35 Lectures
02:33:40
+
Lessons from History - Time Series Analysis
9 Lectures 26:59

This video provides an overview of the entire course.                    

Preview 03:48

The base R package provides date functionality. This video will show you several date-related operations in R.

Creating and Examining Date Objects
03:57

R supports many useful manipulations with date objects, such as date addition and subtraction, and the creation of date sequences.

Operating On Date Objects
02:57

Before creating proper time series objects, we will learn some preliminary analyses.

Performing Preliminary Analyses on Time Series Data
02:39

In this video, we will look at various features to create and plot time-series objects. We will consider data with single and multiple time series.

Using Time Series Objects
04:31

This video covers the use of the decompose and stl functions to extract the seasonal, trend, and random components of time series.

Decomposing Time Series
02:45

This video shows how we can use the filter function from the stats package to compute moving averages.

Filtering the Time Series Data
01:36

The stats package contains functionality for applying the HoltWinters method for exponential smoothing in the presence of trends and seasonality.

Smoothing and Forecasting Using the Holt-Winters Method
02:13

The forecast package provides the auto.arima function to fit the best ARIMA models for a univariate time series.

Building an Automated ARIMA Model
02:33
+
It's All About Your Connections – Social Network Analysis
4 Lectures 29:03

In this video, we’ll cover the process of downloading data from Meetup.com using their public API.

Preview 08:23

In this application, the nodes represent the users of Meetup.com and an edge connects two nodes if they are members of at least one common group.

Creating Adjacency Matrices and Edge Lists
05:22

This video covers the features in the igraph package to create graph objects, plot them, and extract network information from graph objects.

Plotting Social Network Data
09:46

This video covers the methods used to compute some of the common metrics used on social networks.

Computing Important Network Metrics
05:32
+
Put Your Best Foot Forward – Document and Present Your Analysis
3 Lectures 25:08

R Markdown provides a simple syntax to define analysis reports. Based on such a report definition, knitr can generate reports in HTML, PDF, Microsoft Word format, and several presentation formats.

Preview 11:03

This video illustrates the main components of a shiny application through some examples.

Creating Interactive Web Applications with Shiny
09:01

Rpres, built into RStudio, enables you to create PDF slide presentations of your data analysis. In this video, we’ll develop a small application that showcases the important Rpres features.

Creating PDF Presentations of Your Analysis with R Presentation
05:04
+
Work Smarter, Not Harder – Efficient and Elegant R Code
6 Lectures 25:11

The function can either be a built-in R function or a custom function. In your own code, before you resort to a loop to process all 

the elements of a vector

Preview 03:59

The apply function can apply a user-specified function to all the rows or columns of a matrix and return an appropriate collection with the results.

Processing Entire Rows or Columns Using the Apply Function
03:00

The lapply function works on objects of type vector, list, or data frame. It applies a user-specified function to each element of the 

passed-in object and returns a list of the results.

Applying a Function to All the Elements of a Collection with lapply and sapply
03:35

The tapply function applies a function to each partition of the dataset. Hence, when we need to evaluate a function over subsets of a vector defined by a factor, tapply comes in handy.

Applying Functions to the Subsets of a Vector
02:08

Many data analysis tasks involve first splitting the data into subsets, applying some operation on each subset, and then combining the results suitably. The plyr package provides simple functions to apply this pattern while simplifying the specification of the object types through systematic naming of the functions.

Using the split-apply-combine Strategy with plyr
04:11

In this video, we will cover data.table, which processes large amounts of data very efficiently without us having to write detailed 

procedural code.

Slicing, Dicing, and Combining Data with Data Tables
08:18
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Where in the World? – Geospatial Analysis
8 Lectures 24:29

We will use the RgoogleMaps package to get and plot Google maps of specific areas based on latitude and longitude.

Preview 03:04

RgoogleMaps also allows you to overlay your own data points on static maps. In this video, we will use a data file to plot a Google map of the general area covered by the data points

Overlaying Data on the Downloaded Google Map
03:42

Using RgoogleMaps is easy, and we have seen that it offers very little control over map elements and plotting. Importing shape files, on the other hand, gives us total control.

Importing ESRI Shape Files into R
02:51

The sp package has the necessary features to store and plot geographic data. In this recipe, we will use the sp package to plot 

imported shape files.

Using the sp Package to Plot Geographic Data
02:16

The maps package has several pre-built maps that we can download and adapt. This video demonstrates the capabilities of these maps.

Getting Maps from the Maps Package
02:44

When you have a regular data frame that has spatial attributes in addition to other attributes, processing them becomes easier if you convert them to full-fledged spatial objects.

Creating Spatial Data Frames from Regular Data Frames Containing Spatial & Other
01:46

In order to display geographical aspect information on a map representation, we need to embellish the basic data with enough 

geographic coordinate information for plotting.

Creating Spatial Data Frames by Combining Regular Data Frames with SpatialObject
05:51

This video shows how we can add variables to spatial data frame objects. We can also add nonspatial variables to an existing spatial data frame object.

Adding Variables to an Existing Spatial Data Frame
02:15
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Playing Nice – Connecting to Other Systems
5 Lectures 22:50

Sometimes, we may develop parts of an application in Java and may need to access them from R. The rJava package allows us to access Java objects directly from within R.

Preview 07:29

The JRI allows us to execute R commands inside Java applications as a single thread. JRI loads R libraries into Java and thus provides a Java API to R functions.

Using JRI to Call R Functions from Java
04:32

In this video, we’ll execute an R script from Java and read the results from R into Java for further processing.

Executing R Scripts from Java
02:40

There are multiple packages to connect to Excel with R; in this recipe, we’ll discuss the xlsx package. Other commonly used packages are RExcel and XLConnect.

Using the XLSX Package to Connect to Excel
04:36

The fluid state of NoSQL databases means that no such standard approach has yet evolved. We illustrate this with MongoDB using the rmongodb package.

Reading Data From NoSQL Databases – MongoDB
03:33

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Packt Publishing
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