Text Analytics/Text Mining Using R
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Text Analytics/Text Mining Using R

Data Science Text Analytics/Text Mining Using R
4.1 (62 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.
1,246 students enrolled
Created by ExcelR Solutions
Last updated 6/2017
English
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Current price: $10 Original price: $30 Discount: 67% off
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Includes:
  • 1.5 hours on-demand video
  • 1 Article
  • 9 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Perform text mining applications using structured & unstructured data;
  • Understand about document term matrix, term frequency, term frequency inverse document, term frequency for normalizing
  • Differentiate between size of word which indicates the frequency of the said word in a word cloud, clustering based on related use for better insights and how to read the results in context to make sense of the word
  • Understand from a practical case study the various steps of text mining in R and the use of Positive and negative word banks
  • Learn Web and Social media extraction using R, Risk sensing - sentiment analysis, Twitter application management for extracting tweets
  • Understand the clustering concept, that is an integral part of text mining
View Curriculum
Requirements
  • Download R & RStudio before starting this tutorial
  • Download datasets folder in zipfile which is uploaded in session 1
  • While it is not an essential prerequisite, it will be a good idea to go through our course on “Data Mining – Clustering Segmentation Using R, Tableau before going through this course
Description

During this course you will be introduced to one of the most important and fast catching up data mining concept. The need for making sense of unstructured data and the knowledge of the various tools is of paramount importance.

  1. Text mining is the first step in data mining of unstructured data.
  2. As part of this course you will be introduced to the various stages of text mining
  3. Understand about word cloud, clustering, and making analysis based on context,
  4. Use of Negative and positive words banks for relational analysis
  5. Work with a live example of extraction of data from Web and perform all the facets of text mining using R
  6. Learn Web and Social media extraction using R, Risk sensing - sentiment analysis, Twitter application management for extracting tweets
Who is the target audience?
  • All the IT professionals, whose experience ranges from '0' onwards are eligible to take this session. Especially professionals from data analysis, data warehouse, data mining, business intelligence, reporting, data science, etc, will naturally fit in well to take this course.
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Curriculum For This Course
12 Lectures
01:43:10
+
Text Mining Introduction
6 Lectures 01:04:03

Introduction to Text mining, Key takeaways of this course, why Text mining and sources of textual data, Bag of Words

Preview 10:44

Learn in Understanding document and corpus; stemming of of words; Positive and negative features of a review

Terminology And Preprocessing Of Data
06:54

Learn about Document term matrix; Term Frequency; Term Frequency Inverse document Term frequency for normalizing;

DTM And TDM Format
07:37

Learn about Size of word indicates the frequency of the the said word, Clustering based on related users for better insights; to be read in context to make sense of the word;  

Corpus Level Word Cloud
07:27

Brief case study on Clinical Trials project; understanding the Business objective and description; Unigram, Word cloud, cluster dendrogram; Bi gram Word cloud & semantic network;

Case Study On Real Project Part- 1
17:10

Learn Practical case study using R, The various steps of Text mining in R; Positive and negative words;

Case Study On Real Project Part- 2
14:11
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Text Analytics Using R
4 Lectures 24:43

Learn Web and Social media extraction using R; Risk sensing - sentiment analysis; Twitter application management for extracting tweets;

Twitter Data Extraction Using R
15:16

Learn Extracting reviews from Amazon using R; Identifying the common start and common end in the source of the review

Amazon Data Extraction Using R
05:28

Learn to Analyse unstructured data; Corpus; document; data cleansing; quirks; stemming; Document term matrix, term frequency; Generate positive and negative word cloud; Natural Language processing  

Recap Text Mining Attached With Assignment
03:53

Quiz-1
7 questions

Bonus Section....Whats Next..?
00:06
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Optional Topic of Data Analytics
2 Lectures 14:28

Learn about The main difference between Hierarchical and non-hierarchical clustering; similarity within, dissimilarity amongst clusters; Algorithm (or iterative steps) for K means clustering

K Means Clustering Introduction
04:44

Visualization of Hierarchical clustering, Grouping of records & division of cluster based on distance measure. Rules for the measure of distance

Hierarchical Clustering Introduction
09:44
About the Instructor
ExcelR Solutions
4.2 Average rating
683 Reviews
7,000 Students
8 Courses
Pioneer in professional management trainings & consulting

Certifications:

Certified Six Sigma Master Black Belt

Project Management Professional (PMP)

Agile Certified Practitioner (PMI - ACP)

Risk Management Professional (PMI-RMP)

Certified Scrum Master

Agile Project Management – Foundation & Practitioner from APMG

Bharani Kumar is an Alumnus of premier institutions like IIT & ISB with 15+ years professional experience and worked in various MNCs such as HSBC, ITC, Infosys, Deloitte in various capacities such as Data Scientist, Project Manager, Service Delivery Manager, Process Consultant, Delivery Head etc.

He has trained over 1500 professionals across the globe on Business Analytics, Agile, PMP, Lean Six Sigma, Business analytics and the likes.

He has 8 years of extensive experience in corporate, open house and online training.

He is a thorough implementer with abilities in Business Analytics and Agile projects.

He worked in Delivery management focusing on maximizing business value articulation.

He has a comprehensive experience in leading teams and multiple projects.

Quality Management: A thorough implementer with abilities in Quality management focusing on maximizing customer satisfaction, process compliance and business value articulation; comprehensive experience in leading teams & multiple projects. A result-oriented leader with expertise in devising strategies aimed at enhancing overall organizational growth, sustained profitability of operations and improved business performance.

Project Management: Project Management Professional involved in Initiation, Planning, Execution, Monitoring & Controlling and Closing phases of project activities. Devising and implementing project plans within preset budgets and deadlines and managing the projects towards successful delivery of project deliverables and meeting project objectives.

Training: Close to 8 years training experience and conducted multiple trainings in PMP, Agile, Six Sigma, Business Analytics and Process Excellence across the globe. Understands the individual differences of the attendees and possesses excellent training skills and considered as one of the best trainers in his areas of expertise.