More Data Mining with R

BY
Udemy

Learn how to perform market basket analysis, analyze social networks, and mine Twitter data, text, and time series data.

Mode

Online

Fees

₹ 549 1499

Quick Facts

particular details
Medium of instructions English
Mode of learning Self study
Mode of Delivery Video and Text Based

Course overview

Companies mine data for insights. Businesses can use software to find customer patterns in large data sets to improve marketing, sales, and costs. Gathering, storing, and processing data are all necessary steps in data mining. Each piece of data is mined in a series of five steps. Information is gathered and stored in data warehouses. Finally, they use local or remote servers to keep everything organized and safe. Information technology, business analysis, and management coordinate the data organization. The data is then organized by the app based on the user's input, and the final product can be shared with others in the form of a table or graph. More Data Mining with R certification is made available by Udemy to candidates who want to master market basket analyses and association analysis concepts.

More Data Mining with R online training Includes 10.5 hours of video, 11 downloadable resources, and a digital certificate upon course completion. More Data Mining with R online classes incorporates an introduction to R and data mining,  association analysis, association rules and lift, social network analysis, preprocessing Twitter data, string manipulation, and time series

The highlights

  • Full Lifetime Access
  • 10.5 hours on-demand video
  • 11 Downloadable Resources
  • Access on Mobile and TV
  • Certificate of Completion

Program offerings

  • Online course
  • Learning resources
  • 30-day money-back guarantee
  • Unlimited access

Course and certificate fees

Fees information
₹ 549  ₹1,499
certificate availability

Yes

certificate providing authority

Udemy

What you will learn

Measurement skills Knowledge of data mining

More Data Mining with R certification course, the applicant will learn about understanding the conceptual foundations of association analysis and perform market basket analysis, and be able to create visualizations of social networks using the iGraph package. The applicant will gain knowledge about how to examine and mine social network data to understand all of the implicit relationships. The candidate will get to know my text data to create word association visualizations, term documents with word frequency counts and associations, and create word clouds, as well as learn how to process text and string data, including the use of 'regular expressions and extract prototypical information about cycles from time series data.

The syllabus

Introduction to R and to Data Mining

  • Welcome to More Data Mining with R !
  • Course Preliminaries
  • Data Input and Output (part 1)
  • Data Input and Output (part 2)
  • More R Scripting and Visualizations (part 1)
  • More R Scripting and Visualizations (part 2)
  • More Input and Output (part 1)
  • More Input and Output (part 2)
  • Homework Exercise: Execute Second Set of Scripts on your Own

Association Analysis (part 1)

  • Introduction to Association Analysis (part 1)
  • Introduction to Association Analysis (part 2)
  • Preparing the Titanic Dataset
  • Rule Mining with Titanic Dataset (part 1)
  • Rule Mining with Titanic Dataset (part 2)
  • Interpreting Rules
  • Visualizing Association Rules (part 1)
  • Visualizing Association Rules (part 2)

Association Analysis: Online Radio and Predicting Income

  • Association Rules and Lift Reviewed
  • Association Rules Reviewed (part 2)
  • Online Radio Predictor Example (part 1)
  • Online Radio Predictor Example (part 2)
  • Predicting Income Example (part 1)
  • Predicting Income Example (part 2)
  • Predicting Income Example (part 3)

Social Network Analysis: iGraph Visualizations

  • Introduction to iGraph
  • iGraph Visualization Examples (part 1)
  • iGraph Visualization Examples (part 2)
  • iGraph Measurement Examples (part 3)
  • iGraph Measurement Examples (part 4)
  • iGraph Visualization Examples (part 5)
  • iGraph Visualization Examples (part 6)
  • iGraph Visualization Examples (part 7)

Social Network Analysis (part 2)

  • Visual Network Basics Revisited
  • Visual Network: Marriage and Power in 15th Century Florence (part 1)
  • Visual Network: Marriage and Power in 15th Century Florence (part 2)
  • Example: Friendship Network (part 1)
  • Example: Friendship Network (part 2)
  • Example: Friendship Network (part 3)

Text Mining Twitter Data

  • Preprocessing Twitter Data
  • Transforming Twitter Data
  • Stemming and Frequency Counts
  • Building a Text Term Document
  • Frequent Terms and Associations
  • Word Cloud and Word Clustering
  • K-Means and K-Medoids Clustering
  • Using Lists for Text Processing (part 1)
  • Using Lists for Text Processing (part 2)
  • Using Lists for Text Processing (part 3)

Text (String) Manipulation

  • Introduction to String Manipulation (slides, part 1)
  • Introduction to String Manipulation (slides, part 2)
  • Text and String Manipulation Script Demos (part 1)
  • Text and String Manipulation Demos (part 2)
  • Text and String Manipulation Demos (part 3)
  • Text and String Manipulation Demos (part 4)
  • Regular Expression Basics (slides and script)
  • More Advanced Regular Expression Capabilities (slides and script)

Time Series Data Mining

  • Maine Unemployment Data (part 1)
  • Maine Unemployment Data (part 2)
  • Airline Travel Example
  • Electric Consumption in Australia (part 1)
  • Electric Consumption in Australia (part 2)
  • Time Series Clustering (part 1)
  • Time Series Clustering (part 2)
  • Time Series Classification

Case Study: Forecasting House Price Indices in Canberra, Australia

  • Forecasting House Prices: Exploring the Data (part 1)
  • Forecasting House Prices: Exploring the Data (part 2)
  • Forecast House Prices: Use Trend and Seasonal Components

Instructors

Dr Geoffrey Hubona
Associate Professor
Freelancer

B.E /B.Tech, Other Bachelors, Other Masters, Ph.D, M...

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