R Data Analysis Time-Series and Social Media

R Data Analysis – Time-Series and Social Media, available at $19.99, has an average rating of 3.4, with 35 lectures, 1 quizzes, based on 10 reviews, and has 69 subscribers.

You will learn about 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 This course is ideal for individuals who are This course is for anyone who wants to learn analytical techniques from scratch. It is particularly useful for This course is for anyone who wants to learn analytical techniques from scratch.

Enroll now: R Data Analysis – Time-Series and Social Media

Summary

Title: R Data Analysis – Time-Series and Social Media

Price: $19.99

Average Rating: 3.4

Number of Lectures: 35

Number of Quizzes: 1

Number of Published Lectures: 35

Number of Published Quizzes: 1

Number of Curriculum Items: 36

Number of Published Curriculum Objects: 36

Original Price: $109.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

This course is for anyone who wants to learn analytical techniques from scratch.

Target Audiences

This course is for anyone who wants to learn analytical techniques from scratch.

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.

Course Curriculum

Chapter 1: Lessons from History – Time Series Analysis

Lecture 1: The Course Overview

Lecture 2: Creating and Examining Date Objects

Lecture 3: Operating On Date Objects

Lecture 4: Performing Preliminary Analyses on Time Series Data

Lecture 5: Using Time Series Objects

Lecture 6: Decomposing Time Series

Lecture 7: Filtering the Time Series Data

Lecture 8: Smoothing and Forecasting Using the Holt-Winters Method

Lecture 9: Building an Automated ARIMA Model

Chapter 2: Its All About Your Connections – Social Network Analysis

Lecture 1: Downloading Social Network Data Using Public APIs

Lecture 2: Creating Adjacency Matrices and Edge Lists

Lecture 3: Plotting Social Network Data

Lecture 4: Computing Important Network Metrics

Chapter 3: Put Your Best Foot Forward – Document and Present Your Analysis

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

Lecture 2: Creating Interactive Web Applications with Shiny

Lecture 3: Creating PDF Presentations of Your Analysis with R Presentation

Chapter 4: Work Smarter, Not Harder – Efficient and Elegant R Code

Lecture 1: Exploiting Vectorized Operations

Lecture 2: Processing Entire Rows or Columns Using the Apply Function

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

Lecture 4: Applying Functions to the Subsets of a Vector

Lecture 5: Using the split-apply-combine Strategy with plyr

Lecture 6: Slicing, Dicing, and Combining Data with Data Tables

Chapter 5: Where in the World? – Geospatial Analysis

Lecture 1: Downloading and Plotting a Google Map of an Area

Lecture 2: Overlaying Data on the Downloaded Google Map

Lecture 3: Importing ESRI Shape Files into R

Lecture 4: Using the sp Package to Plot Geographic Data

Lecture 5: Getting Maps from the Maps Package

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

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

Lecture 8: Adding Variables to an Existing Spatial Data Frame

Chapter 6: Playing Nice – Connecting to Other Systems

Lecture 1: Using Java Objects in R

Lecture 2: Using JRI to Call R Functions from Java

Lecture 3: Executing R Scripts from Java

Lecture 4: Using the XLSX Package to Connect to Excel

Lecture 5: Reading Data From NoSQL Databases – MongoDB

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 2 votes

2 stars: 0 votes

3 stars: 3 votes

4 stars: 3 votes

5 stars: 2 votes

Frequently Asked Questions

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