Data Science:Data Mining & Natural Language Processing in R course will teach students how to use R for machine learning tasks like clustering, classification, and regression as well as pre-processing and visualisation. Students will get a competitive advantage for themselves and to their business, Students will also be able to extract insights from text data and Twitter.

The course gives an in-depth knowledge to Text Mining, Natural Language Processing, and Data Science in R. Students are thus given a limited understanding of the subject. In contrast to other courses, the course doesn’t stop at machine learning but also teaches about text data mining on social media platforms like Twitter and Facebook, The course also talk about data mining, web scraping, text mining, and natural language processing. Students doesn’t require any previous knowledge to statistics or machine learning. The courses are usually available at INR 3,499 on Udemy but you can click now to get 87% off and get Data Science:Data Mining & Natural Language Processing in R for INR 449.

Who all can opt for this course?

  • Students looking to get experience using R for practical data science and machine learning
  • Students who want to master the fundamental principles and practical uses of data mining in R
  • Students who are interested in collecting or mining data from Twitter, for example
  • Students who are interested in preparing and displaying actual data
  • students who want to examine and draw conclusions from textual information
  • Students who want to understand the fundamentals of R’s Natural Language Processing (NLP)

Course Highlights

Key HighlightsDetails
Registration LinkApply Now!
PriceINR 449 (INR 3,49987 % off
Duration13 Hours
Student Enrollment3,932 students
InstructorMinerva Singh
Topics CoveredText Mining, Data Minining, Machine Learning, Natural Language Processing, R Programming, and Data Science
Course LevelBeginner
Total Student Reviews367

Learning Outcomes

  • Run R to complete the most crucial pre-processing tasks required before machine learning
  • Apply R to data visualisation
  • For unsupervised classification in R, use machine learning
  • Create R models for classification and regression to do supervised learning
  • Analyze and contrast the performance of supervised machine learning algorithms in R
  • Use R to perform sentiment analysis on text data

Course Content

S.No.Module (Duration)Topics
1.INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools (19 minutes)Introduction
Data and Scripts For the Course
Introduction to R and RStudio
Start with Rattle
Troubleshooting For Rattle
Conclusion to Section 1
2.Reading in Data from Different Sources in R (43 minutes)Read in Data from CSV and Excel Files
Read Data from a Database
Read Data from JSON
Read in Data from Online CSVs
Read in Data from Online HTML Tables-Part 1
Read in Data from Online HTML Tables-Part 2
Read Data from Other Sources
Conclusions to Section 2
3.Exploratory Data Analysis and Data Visualization in R (02 hours 16 minutes)Remove NAs
More Data Cleaning
Exploratory Data Analysis(EDA): Basic Visualizations with R
More Exploratory Data Analysis with xda
Introduction to dplyr for Data Summarizing-Part 1
Introduction to dplyr for Data Summarizing-Part 2
Data Exploration & Visualization With dplyr & ggplot2
Pre-Processing Dates-Part 1
Pre-Processing Dates-Part 2
Plotting Temporal Data in R
Twist in the (Temporal) Data
Associations Between Quantitative Variables- Theory
Testing for Correlation
Evaluate the Relation Between Nominal Variables
Cramer’s V for Examining the Strength of Association Between Nominal Variable
Section 3 Quiz
4.Data Mining for Patterns and Relationships (37 minutes)What is Data Mining?
Association Mining with Apriori
Apriori with Real Data
Visualize the Rules
Association Mining with Eclat
Eclat with Real Data
5.Machine Learning for Data Science (11 minutes)How is Machine Learning Different from Statistical Data Analysis?
What is Machine Learning (ML) About? Some Theoretical Pointers
6.Unsupervised Classification- R (01 hour 04 minutes)K-means Clustering
Fuzzy K-Means Clustering
Weighted K-Means Clustering
Hierarchical Clustering in R
Expectation-Maximization (EM) in R
Use Rattle for Unsupervised Clustering
Conclusions to Section 6
Section 6 Quiz
7.Dimension Reduction (56 minutes)Dimensionality Reduction-theory
Removing Highly Correlated Predictor Variables
Variable Selection Using LASSO Regression
Variable Selection With FSelector
Boruta Analysis for Feature Selection
Conclusions to Section 7
Section 7 Quiz
8.Supervised Learning Theory (13 minutes)Some Basic Supervised Learning Concepts
Pre-processing for Supervised Learning
9.Supervised Learning: Classification (01 hour 57 minutes)Binary Classification
What are GLMs?
Logistic Regression Models as Binary Classifiers
Linear Discriminant Analysis (LDA)
Binary Classifier with PCA
Obtain Binary Classification Accuracy Metrics
Multi-class Classification Models
Our Multi-class Classification Problem
Classification Trees
More on Classification Tree Visualization
Decision Trees
Random Forest (RF) classification
Examine Individual Variable Importance for Random Forests
GBM Classification
Support Vector Machines (SVM) for Classification
More SVM for Classification
Conclusions to Section 9
Section 9 Quiz
10.Supervised Learning: Regression (01 hour 14 minutes)Ridge Regression in R
LASSO Regression in R
Generalized Additive Models (GAMs) in R
Boosted GAMs
MARS Regression
CART-Regression Trees in R
Random Forest (RF) Regression
GBM Regression
Compare Models
Conclusions to Section 10
11.Introduction to Artificial Neural Networks (ANN) (43 minutes)What are Artificial Neural Networks?
Neural Network for Binary Classifications
Neural Network with PCA for Binary Classifications
Neural Network for Regression
More on Neural Networks- with neuralnet
Identify Variable Importance in Neural Networks
12.More Web-scraping and Text Data Mining (45 minutes)Read in Text Data from an HTML Page
Explore Amazon with R
More Webscraping With rvest-IMDB Webpage
Prior to Mining Data from Twitter
Extract Tweets Using R
More Twitter Data Extraction Using R
Get Data from Facebook Using R
Conclusions to Section 12
13.Gaining Insights from Text Data- Text Mining and Natural Language Processing (NL (01 hour 28 minutes)Explore Tweet Data
Visualize Tweet Sentiment Wordcloud- India’s Demonetization Policy
More Wordclouds: Amazon Review Data
Word Frequency in Text Data
Tweet Sentiments- India’s Demonetization Policy
Sentiment Analysis of Mugabe Tweets
Tweet Sentiments- Mugabe’s Ouster
Examine the Polarity of Text
Polarity of Individual Tweets
Topic Modelling a Document
Topic Modelling Multiple Documents
Conclusions to Section 13
14.Text Data and Machine Learning (24 minutes)EDA With Text Data
Identify Deceptive Reviews With Supervised Classification
Identify Spam Emails with Supervised Classification
15.Miscellaneous Lectures (19 minutes)3D Scatterplots
Getting Acquainted with Github Desktop
Data Editing Within R
Group By Time

Resources Required

  • A strong desire to learn more about data mining and data science
  • Keen interest in extracting insights from text data and text mining
  • Should have previous R and RStudio experience
  • In R, packages ought to be able to be installed and read
  • It will be advantageous but not required to have prior knowledge of the fundamentals of statistical data analysis, data visualisation, and summarization in R

Featured Review

 Anonymized U. (5/5) : The course is beyond my expectations. It will enhance the quality of my work several notches.


  • Kitara Beily (5/5) : Exclusive one! This guy is best presenter of tech topics I’ve ever seen online.
  • Anonymized User (5/5) : The concept has been brilliantly explained by the instructor with practical examples.
  • Salma Rabiia (5/5) : It was a great course where I learned a lot about data science.
  • Sita Kimic (5/5) : This course is great if you want to grasp the concept of data science .


  • O. L. (1.5/5) : The software does not install, outdated info
  • Mauricio L. (1.5/5): The instructutor does not provide the necesaary guides as other courses about how to install correctly the proper material for the course
  • Taskyn R. (1.5/5) : Beginning was very good. Bit when it comes to supervised learning it become mess.Also Author dosenot writes code online.Somecases not executes functions,which was written.
  • Prashant B. (1/5) : It is not taught properly. what kind of method are used, why it is used,no proper explanation. disappointing

About the Author

The instructor of this course is Minerva Singh who is a Bestselling Instructor & Data Scientist(Cambridge Uni). With 4.4 Instructor Rating and 18,466 Reviews on Udemy, he/she offers 50 Courses and has taught 93,130 Students so far.

  • The instructor finished his Doctorate in 2017 at the University of Cambridge in the UK, where he concentrated on using data science tools to calculate the effects of forest loss on tropical ecosystems
  • Instructor graduated from Oxford University with an MPhil in the School of Geography and Environment and an MSc in the Department of Engineering
  • Instructor have more than ten years of experience conducting academic research (published in prestigious international peer-reviewed scientific journals like PLOS One) and offering advice to non-governmental and business stakeholders on matters relating to data science, deep learning, and earth observation (EO)
  • Instructor have a proven track record of using R and Python to accomplish machine learning, data visualisation, spatial data analysis, deep learning, and NLP applications
  • Instructor have developed my statistical and data analysis skills through a variety of MOOCs, including The Analytics Edge (an R-based statistics and machine learning course offered by EdX), Statistical Learning (an R-based Machine Learning course offered by Stanford online), and the IBM Data Science Professional certificate Track
  • Instructor have also received his education from some of the best universities in the world
  • Instructor have a wide range of specialties, including deep learning (Tensorflow, Keras), machine learning, spatial data analysis (including processing EO data), data visualisation, natural language processing, and financial analysis, among others
  • Instructor have served as a peer reviewer for esteemed academic publications like Remote Sensing and delivered guest talks at illustrious gatherings like the Open Data Science Conference (ODSC).

Comparison Table

ParametersData Science:Data Mining & Natural Language Processing in RRegression Analysis for Statistics & Machine Learning in RPractical Neural Networks & Deep Learning In R
OffersINR 455 (INR 3,499) 87% offINR 455 (INR 3,499) 87% offINR 455 (INR 3,499) 87% off
Duration13.5 hours7.5 hours5.5 hours
Rating5.0 /54.9 /54.4 /5
Student Enrollments3,9324,4081,770
InstructorsMinerva SinghMinerva SinghMinerva Singh
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