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3.33 out of 5
3.33
9 reviews on Udemy

R Data Analysis – Time-Series and Social Media

Master this practical approach to performing analytical operations
Instructor:
Packt Publishing
56 students enrolled
English [Auto]
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

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.

Lessons from History - Time Series Analysis

1
The Course Overview

This video provides an overview of the entire course.                    

2
Creating and Examining Date Objects

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

3
Operating On Date Objects

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

4
Performing Preliminary Analyses on Time Series Data

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

5
Using Time Series Objects

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.

6
Decomposing Time Series

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

7
Filtering the Time Series Data

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

8
Smoothing and Forecasting Using the Holt-Winters Method

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

9
Building an Automated ARIMA Model

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

It's All About Your Connections – Social Network Analysis

1
Downloading Social Network Data Using Public APIs

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

2
Creating Adjacency Matrices and Edge Lists

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.

3
Plotting Social Network Data

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

4
Computing Important Network Metrics

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

Put Your Best Foot Forward – Document and Present Your Analysis

1
Generating Reports of Your Data Analysis with R Markdown and knitR

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.

2
Creating Interactive Web Applications with Shiny

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

3
Creating PDF Presentations of Your Analysis with R Presentation

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.

Work Smarter, Not Harder – Efficient and Elegant R Code

1
Exploiting Vectorized Operations

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

2
Processing Entire Rows or Columns Using the Apply Function

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.

3
Applying a Function to All the Elements of a Collection with lapply and sapply

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.

4
Applying Functions to the Subsets of a Vector

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.

5
Using the split-apply-combine Strategy with plyr

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.

6
Slicing, Dicing, and Combining Data with Data Tables

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

procedural code.

Where in the World? – Geospatial Analysis

1
Downloading and Plotting a Google Map of an Area

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

2
Overlaying Data on the Downloaded Google Map

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

3
Importing ESRI Shape Files into R

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.

4
Using the sp Package to Plot Geographic Data

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.

5
Getting Maps from the Maps Package

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

6
Creating Spatial Data Frames from Regular Data Frames Containing Spatial & Other

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.

7
Creating Spatial Data Frames by Combining Regular Data Frames with SpatialObject

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.

8
Adding Variables to an Existing Spatial Data Frame

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.

Playing Nice – Connecting to Other Systems

1
Using Java Objects in R

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.

2
Using JRI to Call R Functions from Java

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.

3
Executing R Scripts from Java

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

4
Using the XLSX Package to Connect to Excel

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.

5
Reading Data From NoSQL Databases – MongoDB

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

6
Test Your Knowledge
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3 hours on-demand video
Certificate of Completion

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